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In the article the period of traditional education of aboriginal people in Canada in precolonial times has been presented. The main objectives have been defined as theoretical analysis of scientific and pedagogical literature, which highlights different aspects of the problem under research; characteristic of theoretical framework in understanding the concept of traditional aboriginal pedagogy and main principles underlying the education of younger generations of the indigenous people in Canada. The major components of teaching methods (practical, visual and oral) have been specified. Practical, visual and oral methods of imparting knowledge have been discussed and peculiarities of the traditional education of native population in Canada in precolonial period have been identified. The problem of traditional education of aboriginal people in Canada has been studied by scientists: aboriginal education (M. Battiste, J. Henderson, J. Lambe); development of aboriginal education (J. Friesen, V. Friesen, J. Miller, E. Neegan); tertiary education of aboriginal people (V. Kirkness); traditional education of aboriginal people (L. McGregor). The research methodology comprises theoretical methods (comparative-historical method; logical and comparative methods; methods of induction and deduction, synthesis and analysis).

Objective This study aimed to compare the predictive values of the American College of Obstetricians and Gynecologists (ACOG), the National Institute for Health and Care Excellence (NICE), and the Society of Obstetricians and Gynecologists of Canada (SOGC) factor-based models for preeclampsia (PE) screening. Study Design We conducted a secondary analysis of maternal and birth data from 32 hospitals. For each delivery, we calculated the risk of PE according to the ACOG, the NICE, and the SOGC models. Our primary outcomes were PE and preterm PE (PE combined with preterm birth) using the ACOG criteria. We calculated the detection rate (DR or sensitivity), the false positive rate (FPR or 1 − specificity), the positive (PPV) and negative (NPV) predictive values of each model for PE and for preterm PE using receiver operator characteristic (ROC) curves. Results We used 130,939 deliveries including 4,635 (3.5%) cases of PE and 823 (0.6%) cases of preterm PE. The ACOG model had a DR of 43.6% for PE and 50.3% for preterm PE with FPR of 15.6%; the NICE model had a DR of 36.2% for PE and 41.3% for preterm PE with FPR of 12.8%; and the SOGC model had a DR of 49.1% for PE and 51.6% for preterm PE with FPR of 22.2%. The PPV for PE of the ACOG (9.3%) and NICE (9.4%) models were both superior than the SOGC model (7.6%; p < 0.001), with a similar trend for the PPV for preterm PE (1.9 vs. 1.9 vs. 1.4%, respectively; p < 0.01). The area under the ROC curves suggested that the ACOG model is superior to the NICE for the prediction of PE and preterm PE and superior to the SOGC models for the prediction of preterm PE (all with p < 0.001). Conclusion The current ACOG factor-based model for the prediction of PE and preterm PE, without considering race, is superior to the NICE and SOGC models. Key Points Clinical factor-based model can predict PE in approximately 44% of the cases for a 16% false positive. The ACOG model is superior to the NICE and SOGC models to predict PE. Clinical factor-based models are better to predict PE in parous than in nulliparous. [ABSTRACT FROM AUTHOR]

Introduction: Early detection of neurodegeneration is essential for optimizing interventions. The highly reproducible progression of neurodegeneration in the decrepit (dcr) mouse allows investigation of early biomarkers and mechanisms of brain injury. Methods: Using high-frequency ultrasound, the common carotid arteries of female and male dcr and control mice were imaged longitudinally at time points bracketing the disease progression (50, 75, and 125 days of age) (n = 6 mice/group/sex). Results: Over the disease time course, the female dcr mice demonstrated increased carotid artery blood flow and pulse wave velocity while the male dcr mice had a decrease in heart rate and no change in carotid artery ultrasound parameters. Early imaging biomarkers were sex-specific, with decreased carotid artery blood flow in female dcr mice and increased carotid artery diameter and decreased pulse wave velocity in males. Discussion: Carotid artery and wave reflection ultrasound is a promising screening tool for early detection of neurodegeneration. [ABSTRACT FROM AUTHOR]

Background: Efforts to preoperatively risk stratify and optimize patients before pancreaticoduodenectomy continue to improve outcomes. This study aims to determine the impact of hypoalbuminemia on outcomes following pancreaticoduodenectomy and outline optimal hypoalbuminemia cut-off values in this population. Methods: The ACS-NSQIP (2016–2021) database was used to extract patients who underwent pancreaticoduodenectomy, comparing those with hypoalbuminemia (< 3.0 g/L) to those with normal albumin. Demographics and 30-day outcomes were compared. Multivariable modeling evaluated factors including hypoalbuminemia to characterize their independent effect on serious complications, and mortality. Optimal albumin cut-offs for serious complications and mortality were evaluated using receiver-operating characteristic curves. Results: We evaluated 25,848 pancreaticoduodenectomy patients with 2712 (10.5%) having preoperative hypoalbuminemia. Patients with hypoalbuminemia were older (68.2 vs. 65.1; p < 0.0001), and were significantly more likely to be ASA class 4 or higher (13.9% vs. 6.7%; p < 0.0001). Patients with hypoalbuminemia had significantly more 30-day complications and after controlling for comorbidities hypoalbuminemia remained a significant independent factor associated with 30-day serious complications (OR 1.80, p < 0.0001) but not mortality (OR 1.37, p = 0.152). Conclusions: Hypoalbuminemia plays a significant role in 30-day morbidity following pancreaticoduodenectomy. Preoperative albumin may serve as a useful marker for risk stratification and optimization. [ABSTRACT FROM AUTHOR]

different populations have different averages of life-space assessment scores and defining cutoff values of clinical significance by each population should take into consideration. Different cutoffs to define restricted life space have been reported. The most common is a score of 60 points. There are other cutoffs derived from receiver operating characteristic (ROC) analysis and used to classify older adults according to their ability in activities of daily living (ADLs) (52.3 points) or instrumental activities of daily living (IADLs) (56 points); other cutoffs are specialized for people with cognitive impairment (26.75 points) or people with spinal cord injury who need mobility aids (78.5 points). The aims were to identify cutoff points of Life Space Assessment (LSA) in older adults in different sites and to determine the relationship of the cutoff scores with mobility disability and depression. The study population was composed of community-dwelling adults aged 65–74 years who were not institutionalized. An ROC analysis was constructed, and the area under the curve (AUC) was calculated to identify the optimal cutoff that discriminates life-space restriction for each city. Logistic regressions were executed by site to comprehend the association among restricted LSA and mobility disability and depression. In total, 1890 participants were included in the analyses (52.38% women, 37.19% mobility disability and 21.32% had depression). Canada cities had the highest cutoff, while Tirana and Natal had the lowest cutoff (< 50). Kingston was the site with the highest association between life-space restriction and mobility disability (OR 5.4, 95% CI 2.9–10). Saint-Hyacinthe, Tirana, and Manizales had significant associations between depression and restricted life space (OR 3.25, 95% CI 1.53–6.89, OR 3.14, 95% CI 1.88–5.24, and OR 3.99, 95% CI 2.28–6.98, respectively). Different cutoffs to define restricted life-space have been identified in elderly people at different sites. The analysis of the relationship between the restricted life space and personal characteristics like depression and mobility disability supported these findings. The groupings produced by the cutoff points for each site showed notable variations. These findings emphasize the importance of population-based cutoffs to improve the general applicability of LSM criteria and take into consideration the importance of site-specific approaches. [ABSTRACT FROM AUTHOR]

Objectives: Eustachian tube dysfunction (ETD) is a common disease associated with chronic otitis media. A standard diagnostic tool for ETD in patients with tympanic membrane perforation is still lacking. We developed and validated a new diagnostic model for ETD in patients with tympanic membrane perforation. Methods: A prospective study was conducted in patients who had tympanic membrane perforation from February to August 2023. We collected clinical characteristics and examination results including otoscopy, nasal endoscopy, tubomanometry, and 5-item Eustachian Tube Score (ETS-5). Univariate and multivariate logistic regression analysis was performed to determine the independent diagnostic factors. Based on this, the nomogram model was constructed. The discrimination and calibration of the nomogram were evaluated using the area under the curve (AUC), the C-index, the calibration curve, and the decision curve analysis (DCA). Results: A total of 40 participants were enrolled in the study. ETS-5 score and Eustachian tube opening mucosa inflammation in the nasopharynx were significant predictors in identifying ETD. Based on the above independent predictors, a diagnostic nomogram was successfully established. The sensitivity and specificity of the diagnostic model were 80.0% and 90.0%, respectively. The AUC and the C-index of the diagnostic model were both 0.901, which suggested that the model had a good discrimination power. The calibration curve indicated a good calibration degree of the model. DCA showed that the proposed model was useful for clinical practice. Conclusion: The nomogram model is effective and reliable in identifying ETD in patients with tympanic membrane perforation. [ABSTRACT FROM AUTHOR]

Background: The need for cognitive testing is increasing with the aging population and the advent of new Alzheimer disease therapies. To respond to the increased demand, the XpressO was developed as a self‐administered digital cognitive prescreening tool that will help distinguish between populations of subjective and objective cognitive impairment according to the Montreal Cognitive Assessment (MoCA). Methods: This is a prospective validation study. XpressO is composed of tasks that assess memory and executive functions. It is validated compared to the digital MoCA as a gold standard. Out of 118 participants screened from the MoCA Clinic and a family practice clinic, 88 met inclusion criteria, two participants had missing data due to incomplete tasks, 86 participants were included in the analysis; the mean age was 70.34 years. A logistic regression model was built, and its accuracy was evaluated by the sensitivity, specificity, and Area Under the Curve (AUC) of the Receiver Operating Characteristic. Results: Analysis showed strong correlation between (1) XpressO memory tasks scores and the MoCA Memory Index Score (p‐values < 0.001), and between (2) XpressO sub‐test scores and MoCA total score (p‐values < 0.005). The AUC for predicting MoCA performance is 0.845. To classify individuals with normal and abnormal MoCA scores, two threshold values were introduced for the total XpressO scores: sensitivity of 91% at a cutoff of 72, specificity of 90% at a cutoff of 42, and an undetermined range in between. Conclusion: XpressO demonstrated high AUC, high sensitivity and specificity to predict cognitive performance compared to the digital MoCA. It may provide efficient cognitive prescreening by identifying individuals who would benefit from further clinical assessments, potentially reducing waiting times and high burden on healthcare clinics. [ABSTRACT FROM AUTHOR]

Importance: Patient-reported outcome measures provide insights into intervention effects on patients. The Canadian Occupational Performance Measure (COPM) emphasizes identifying priorities in daily activity engagement and evaluating an individual's perception of changes over time. Objective: To assess the responsiveness of the COPM and the minimal clinically important difference (MCID) among patients with frozen shoulders. Design: Prospective, single-blind, randomized controlled trial. Setting: Two physical medicine and rehabilitation clinics. Participants: Ninety-four patients with frozen shoulders enrolled in a previous study. Outcomes and Measures: Baseline and 3-mo evaluations of the COPM and other measures. Responsiveness was assessed using effect size (ES) and standardized response mean (SRM). The MCID values were determined through a distribution-based approach, which used the 0.5 standard deviation and ES methods, and an anchor-based approach, which used the receiver operating characteristic curve method. Results: The ES and SRM results indicated that the COPM had high responsiveness. The distribution-based MCID values for COPM Performance and COPM Satisfaction were 1.17 and 1.44, respectively. The anchor-based MCID values were 2.5 (area under the curve [AUC] = 0.78, 95% confidence interval [CI] [0.64–0.91]) and 2.1 (AUC = 0.76, 95% CI [0.60–0.91]), respectively. Conclusions and Relevance: The findings suggest that the COPM is a responsive outcome measure for patients with frozen shoulder. The established MCID values for the COPM can be valuable for interpreting changes in patient performance and satisfaction, thus aiding clinical interventions and research planning. Plain-Language Summary: This is the first study to review the effectiveness of the Canadian Occupational Performance Measure (COPM) to determine the success of occupational therapy interventions for people with a frozen shoulder. The findings suggest that the COPM is an effective and valuable tool for clients with a frozen shoulder to understand their experiences and treatment priorities and to detect meaningful changes in their performance and satisfaction after an occupational therapy intervention. This is the first study to review the effectiveness of the Canadian Occupational Performance Measure (COPM) to determine the success of occupational therapy interventions for people with a frozen shoulder. [ABSTRACT FROM AUTHOR]

Background: Life expectancy prediction is important for end-of-life planning. Established methods (Palliative Performance Scale [PPS], Palliative Prognostic Index [PPI]) have been validated for intermediate- to long-term prognoses, but last-weeks-of-life prognosis has not been well studied. Patients admitted to a palliative care facility often have a life expectancy of less than three weeks. Reliable last-weeks-of-life prognostic tools are needed. Objective: To improve short-term survival prediction in terminally ill patients. Method: This prospective study included all patients admitted to a palliative care facility in Montreal, Canada, over one year. PPS and PPI were assessed until patients' death. Seven prognostic clinical signs of impending death (Short-Term Prognosis Signs [SPS]) were documented daily. Results: The analyses included 273 patients (76% cancer). The median survival time for a PPS ≤20% was 2.5 days, while for a PPS ≥50% it was 44.5 days, for a PPI >8 the median survival was 3.5 days and for a PPI ≤4 it was 38.5 days. Receiver operating characteristic curves showed a high accuracy in predicting survival. Median survival after the first occurrence of any SPS was below one week. Conclusions: This study demonstrated that the PPS and PPI perform well between one week and three months extending their usefulness to shorter term survival prediction. SPS items provided survival information during the last week of life. Using SPS along with PPS and PPI during the last weeks of life could enable a more precise short-term survival prediction across various end-of-life diagnoses. The translation of this research into clinical practice could lead to a better adapted treatment, the identification of a most appropriate care setting for patients, and improved communication of prognosis with patients and families. [ABSTRACT FROM AUTHOR]

Artificial intelligence-enabled electrocardiogram (ECG) algorithms are gaining prominence for the early detection of cardiovascular (CV) conditions, including those not traditionally associated with conventional ECG measures or expert interpretation. This study develops and validates such models for simultaneous prediction of 15 different common CV diagnoses at the population level. We conducted a retrospective study that included 1,605,268 ECGs of 244,077 adult patients presenting to 84 emergency departments or hospitals, who underwent at least one 12-lead ECG from February 2007 to April 2020 in Alberta, Canada, and considered 15 CV diagnoses, as identified by International Classification of Diseases, 10th revision (ICD-10) codes: atrial fibrillation (AF), supraventricular tachycardia (SVT), ventricular tachycardia (VT), cardiac arrest (CA), atrioventricular block (AVB), unstable angina (UA), ST-elevation myocardial infarction (STEMI), non-STEMI (NSTEMI), pulmonary embolism (PE), hypertrophic cardiomyopathy (HCM), aortic stenosis (AS), mitral valve prolapse (MVP), mitral valve stenosis (MS), pulmonary hypertension (PHTN), and heart failure (HF). We employed ResNet-based deep learning (DL) using ECG tracings and extreme gradient boosting (XGB) using ECG measurements. When evaluated on the first ECGs per episode of 97,631 holdout patients, the DL models had an area under the receiver operating characteristic curve (AUROC) of <80% for 3 CV conditions (PTE, SVT, UA), 80–90% for 8 CV conditions (CA, NSTEMI, VT, MVP, PHTN, AS, AF, HF) and an AUROC > 90% for 4 diagnoses (AVB, HCM, MS, STEMI). DL models outperformed XGB models with about 5% higher AUROC on average. Overall, ECG-based prediction models demonstrated good-to-excellent prediction performance in diagnosing common CV conditions. [ABSTRACT FROM AUTHOR]

Simple Summary: This study investigates the influence of various pharmacokinetic parameters on 177Lu-PSMA therapies via physical, biological, and statistical measures. Employing a clinically validated physiologically based pharmacokinetic (PBPK) model, realistic time–activity curves (TACs) in tumors, salivary glands, and kidneys are generated, allowing for the calculation of metrics such as AUC, dose, BED, and fBED. The results demonstrate the significant impact of multiple parameters on the measured outcomes. In addition to the great impact of administered ligand amount, tumor volume, and receptor density, other pharmacokinetic parameters such as rates of radiopharmaceutical association, internalization, and release were identified as key influencers. Notably, alterations in the parameters induced distinct modifications in TAC features, affecting radiobiological, pharmacokinetic, and statistical aspects. These insights contribute to advancing personalized treatment regimens by understanding the key contributions of various parameters towards improving therapeutic efficacy while minimizing radiation-related toxicities. Purpose: To investigate the impact of physiologically based pharmacokinetic (PBPK) parameters on physical, biological, and statistical measures in lutetium-177-labeled radiopharmaceutical therapies (RPTs) targeting the prostate-specific membrane antigen (PSMA). Methods: Using a clinically validated PBPK model, realistic time–activity curves (TACs) for tumors, salivary glands, and kidneys were generated based on various model parameters. These TACs were used to calculate the area-under-the-TAC (AUC), dose, biologically effective dose (BED), and figure-of-merit BED (fBED). The effects of these parameters on radiobiological, pharmacokinetic, time, and statistical features were assessed. Results: Manipulating PBPK parameters significantly influenced AUC, dose, BED, and fBED outcomes across four different BED models. Higher association rates increased AUC, dose, and BED values for tumors, with minimal impact on non-target organs. Increased internalization rates reduced AUC and dose for tumors and kidneys. Higher serum protein-binding rates decreased AUC and dose for all tissues. Elevated tumor receptor density and ligand amounts enhanced uptake and effectiveness in tumors. Larger tumor volumes required dosimetry adjustments to maintain efficacy. Setting the tumor release rate to zero intensified the impact of association and internalization rates, enhancing tumor targeting while minimizing the effects on salivary glands and kidneys. Conclusions: Optimizing PBPK parameters can enhance the efficacy of lutetium-177-labeled RPTs targeting PSMA, providing insights for personalized and effective treatment regimens to minimize toxicity and improve therapeutic outcomes. [ABSTRACT FROM AUTHOR]

Early damage control resuscitation and massive transfusion (MT) protocol activations improve outcomes in trauma patients with hemorrhagic shock, where scores to guide MT prediction are used including: the Assessment of Blood Consumption (ABC), Shock Index (SI), and Revised Assessment of Bleeding and Transfusion (RABT) scores. Our aim was to validate the RABT score in patients from two level I trauma centers in Canada. A retrospective review of adult patients meeting trauma team activation criteria receiving >1 unit of red blood cells (RBCs) within 24 h of admission, from 2015 to 2020, was conducted. A RABT score ≥ 2, ABC score ≥ 2, and Shock Index (SI) ≥ 1 was used to predict MT using both research (≥10 RBCs in 24 h) and clinical (≥3 RBCs in 3 h) definitions. Scores were assessed and compared using sensitivity, specificity, and the area under the receiver operating characteristic (AUROC). We analyzed 514 patients with a mean age of 44.4 (19.2) years and a median injury severity score of 29 [18–38]. For both MT definitions, the RABT score trended towards higher sensitivity and lower specificity compared to ABC score and SI. For both research and clinical definitions of MT, the AUROC for the RABT score was not significantly higher (Research - RABT: 0.673 [0.610–0.735], ABC: 0.642 [0.551–0.734], SI 0.691 [0.625–0.757]; Clinical - RABT: 0.653 [0.608–0.698], ABC: 0.646 [0.600–0.691], SI 0.610 [0.559–0.660]). The RABT score is a valid tool for predicting the need for MTPs, performing similarly with a trend towards higher sensitivity when compared to the ABC score and SI. [ABSTRACT FROM AUTHOR]

Purpose: Using data from a randomized controlled trial for treatment of prescription-type opioid use disorder in Canada, this study examines sensitivity to change in three preference-based instruments [EQ-5D-3L, EQ-5D-5L, and the Health Utilities Index Mark 3 (HUI3)] and explores an oft-overlooked consideration when working with contemporaneous responses for similar questions—data quality. Methods: Analyses focused on the relative abilities of three instruments to capture change in health status. Distributional methods were used to categorize individuals as 'improved' or 'not improved' for eight anchors (seven clinical, one generic). Sensitivity to change was assessed using area under the ROC (receiver operating characteristics) curve (AUC) analysis and comparisons of mean change scores for three time periods. A 'strict' data quality criteria, defined a priori, was applied. Analyses were replicated using 'soft' and 'no' criteria. Results: Data from 160 individuals were used in the analysis; 30% had at least one data quality violation at baseline. Despite mean index scores being significantly lower for the HUI3 compared with EQ-5D instruments at each time point, the magnitudes of change scores were similar. No instrument demonstrated superior sensitivity to change. While six of the 10 highest AUC estimates were for the HUI3, 'moderate' classifications of discriminative ability were identified in 12 (of 22) analyses for each EQ-5D instrument, compared with eight for the HUI3. Conclusion: Negligible differences were observed between the EQ-5D-3L, EQ-5D-5L, and HUI3 regarding the ability to measure change. The prevalence of data quality violations—which differed by ethnicity—requires further investigation. [ABSTRACT FROM AUTHOR]

Human smoking behavior influences exposure to smoke toxicants and is important for risk assessment. In a prospective observational study, the smoking behavior of Marlboro smokers was measured for 36 h. Puff volume, duration, frequency, flow and inter-puff interval were recorded with the portable CReSSmicro™ device, as has often been done by other scientists. However, the use of the CReSSmicro™ device may lead to some registration pitfalls since the method of insertion of the cigarette may influence the data collection. Participants demonstrated consistent individual characteristic puffing behavior over the course of the day, enabling the creation of a personalized puffing profile. These puffing profiles were subsequently used as settings for smoking machine experiments and tar, nicotine and carbon monoxide (TNCO) emissions were generated. The application of human puffing profiles led to TNCO exposures more in the range of Health Canada Intense (HCI)-TNCO emissions than for those of the International Standardization Organization (ISO). Compared to the ISO regime, which applies a low puff volume relative to human smokers, the generation of TNCO may be at least two times higher than when human puffing profiles were applied on the smoking machine. Human smokers showed a higher puffing intensity than HCI and ISO because of higher puffing frequency, which resulted in more puffs per cigarette, than both HCI and ISO., Competing Interests: The authors declare no conflict of interest.

The psychological impact of medical care accessibility during the pandemic has been widely studied, but little attention has been given to Asian immigrants in Canada. This study aimed to fill this literature gap by using a cross-sectional survey, which aimed to evaluate the impact of the COVID-19 pandemic on Chinese immigrants in North America during the second wave of the pandemic. The study focused on Chinese immigrants aged 16 or older in Canada. Covariates included sociodemographic variables, delayed access to medical care (i.e., treatment or health assessment), and other COVID-19 related variables. We used logistic LASSO regression for model selection and multivariate logistic regression models to evaluate the association between delayed access to treatment/health assessment and psychological distress outcome, as measured by the COVID-19 Peritraumatic Distress Index (CPDI). Missing data were handled using multiple imputation. Our study included 746 respondents, with 47.18% in the normal CPDI group and 36.82% in the mild-to-severe CPDI group. Most respondents were originally from Mainland China and residing in Ontario. Over half have stayed in Canada for at least 15 years. The multivariate logistic regression models identified significant risk predictors of psychological distress status: delayed access to medical care (OR = 1.362, 95% CI: 1.078–1.720, p = 0.0095), fear of COVID-19 (OR = 1.604, 95% CI: 1.293–1.989, p < 0.0001), and social loneliness (OR = 1.408, 95%CI: 1.314–1.508, p < 0.0001). Sociodemographic variables and other COVID-19-related variates did not significantly impact the study's outcome. Our findings shed light on the importance of timely medical care access to psychological well-being among Chinese Canadians. Reliable health information, mental health support, and virtual care tailored to immigrants should be considered to mitigate this impact and promote their overall health and well-being. [ABSTRACT FROM AUTHOR]

Background: Biliary obstruction before liver resection is a known risk factor for post-operative complications. The aim of this study was to determine the impact of persistent hyperbilirubinemia following preoperative biliary drainage before liver resection. Methods: The ACS-NSQIP (2016–2021) database was used to extract patients with cholangiocarcinoma who underwent anatomic liver resection with preoperative biliary drainage comparing those with persistent hyperbilirubinemia (> 1.2 mg/dL) to those with resolution. Patient characteristics and outcomes were compared with bivariate analysis. Multivariable modeling evaluated factors including persistent hyperbilirubinemia to evaluate their independent effect on serious complications, liver failure, and mortality. Results: We evaluated 463 patients with 217 (46.9%) having hyperbilirubinemia (HB) despite biliary stenting. Bivariate analysis demonstrated that patients with HB had a higher rate of serious complications than those with non-HB (80.7% vs 70.3%; P = 0.010) including bile leak (40.9% vs 31.8%; P = 0.045), liver failure (26.7% vs 17.9%; P = 0.022), and bleeding (48.4% vs 36.6%; P = 0.010). Multivariable analysis demonstrated that persistent HB was independently associated with serious complications (OR 1.88, P = 0.020) and mortality (OR 2.39, P = 0.049) but not post-operative liver failure (OR 1.65, P = 0.082). Conclusions: Failed preoperative biliary decompression is a predictive factor for post-operative complications and mortality in patients undergoing hepatectomy and may be useful for preoperative risk stratification. [ABSTRACT FROM AUTHOR]

Research question: Can KIDScoreD5 predict which blastocysts have the highest potential for achieving pregnancy? Design: A retrospective cohort study of 670 single fresh or frozen (FET) embryo transfer cycles was conducted between May 2019 and June 2021 at the Ottawa Fertility Centre, Canada. Blastocysts obtained from stimulated eligible cycles and cultured in a time-lapse incubator were selected for transfer or cryopreservation based on Gardner morphological scoring. Implantation and viable pregnancy rates were analysed retrospectively using KIDScoreD5 and Gardner scores associated with the transferred embryos. The predictive power of the KIDScoreD5 and Gardner assessment was evaluated using the average area under the curve (AUC) of the receiver operating characteristic curve. Results: KIDScoreD5 was positively correlated with implantation (r = 0.96, P = 0.002) and viable pregnancy (r = 0.96, P = 0.0001) rates. In fresh embryo transfer cycles, the AUC for implantation rate was significantly higher for KIDScoreD5 compared with Gardner scoring (0.70 versus 0.63, P = 0.03). For FET, significantly higher AUC were calculated for KIDScoreD5 than for Gardner scoring, for both implantation (0.64 versus 0.54, P = 0.002) and viable pregnancy (0.63 versus 0.53, P = 0.002) rates. When the ranking of cryopreserved embryos was based on KIDScoreD5, 46.2% of the FET cycles had at least one unused sibling embryo with a better KIDScoreD5 than the one selected for FET based on Gardner assessment. Conclusions: KIDScoreD5 predicts implantation and viable pregnancy rates of blastocysts better than Gardner morphological assessment in single fresh or cryopreserved embryo transfer cycles. [ABSTRACT FROM AUTHOR]

Past research has shown that remotely sensed spectral information can be used to predict tree health and vitality. Recent developments in unmanned aerial vehicles (UAVs) have now made it possible to derive such information at the tree and stand scale from high-resolution imagery. We used visible and multispectral bands from UAV imagery to calculate a set of spectral indices for 52,845 individual tree crowns within 38 forest stands in western Canada. We then used those indices to predict the mortality of these canopy trees over the following year. We evaluated whether including multispectral indices leads to more accurate predictions than indices derived from visible wavelengths alone and how the performance varies among three different tree species (Picea glauca, Pinus contorta, Populus tremuloides). Our results show that spectral information can be effectively used to predict tree mortality, with a random forest model producing a mean area under the receiver operating characteristic curve (AUC) of 89.8% and a balanced accuracy of 83.3%. The exclusion of multispectral indices worsened the model performance, but only slightly (AUC = 87.9%, balanced accuracy = 81.8%). We found variation in model performance among species, with higher accuracy for the broadleaf species (balanced accuracy = 85.2%) than the two conifer species (balanced accuracy = 73.3% and 77.8%). However, all models overpredicted tree mortality by a major degree, which limits the use for tree mortality predictions on an individual level. Further improvements such as long-term monitoring, the use of hyperspectral data and cost-sensitive learning algorithms, and training the model with a larger and more balanced data set are necessary. Nevertheless, our results demonstrate that imagery from UAVs has strong potential for predicting annual mortality for individual canopy trees. [ABSTRACT FROM AUTHOR]

Surface/ground waters could be polluted when rain-water and/or snow-melt water infiltrate through waste rock piles at mine sites and dissolve secondary minerals (salts) from rock surfaces. It is important to reduce solute loading by the optimal configuration of waste rock piles. This requires the proper definition and determination of the characteristic properties of waste rock piles in terms of metal leaching and, in particular, rate control mechanisms and scaling laws, and their dependence upon configuration variables. For revealing these characteristic properties this paper proposes a pile-scale C-Q relation: C = C s (1 - e -P/Q ), (P ≡ kλβψ), where C and C s are respectively solute concentration and particle's saturation concentration, Q is the flow rate of the water through a waste rock pile, k represents the effective or average dissolution coefficient of a mineral specie from rock surfaces, β represents rock pile depth, λ represents the ratio of the sum of the surface areas of rocks to the volume that the rocks occupy, and ψ is the sum of the cross-sections of water-flow channels in a waste rock pile. The two characteristic properties revealed by the C-Q relation are: (1) P, the product of k, λ, β, and ψ (P ≡ kλβψ), which is the characteristic property of a waste rock pile in terms of metal leaching, named here the solute production potential; and (2) the ratio of P to Q, P/Q, a non-dimensional number, designated as α (α ≡ P/Q), named here the rate control quotient, which is the scaling law and the rate control mechanism indicator. The value of α quantitatively indicates what controls the rate of mineral dissolution, and it also relates smaller-scale metal-leaching testing results to their corresponding full scales. When α becomes small, say α < 0.5, the rate of solute production potential P becomes in control, and the solute loading is nearly independent of Q; when α becomes larger, say α > 2.5, solute concentration would become close to its saturation concentration C s , and Q determines solute loading (that is, the solute loading is proportional to Q). When 0.5 < α < 2.5, both Q and P are in control, a mixed control mechanism. The 20 years of measurements of mine drainage chemistry from the main waste rock piles at the Equity Silver mine, BC, Canada, are used to illustrate how to determine the two characteristic properties P and α, and how well they are able to describe the waste rock piles in terms of metal leaching., (Crown Copyright © 2019. Published by Elsevier B.V. All rights reserved.)

Background: Left atrial (LA) strain is associated with structural remodeling of the LA. Whether there is an association between LA strain obtained by cardiac magnetic resonance imaging (MRI) and new‐onset atrial fibrillation (AF) after ST‐segment elevation myocardial infarction (STEMI) is unclear. Purpose: To investigate the relationship between LA strain and new‐onset AF after STEMI. Study Type: Retrospective. Population: Three hundred and seventy‐nine STEMI patients were enrolled, of which 26 had new‐onset AF. Field Strength/Sequence: 3.0 T, balanced turbo field echo sequence. Assessment: Patients were divided into w/o AF group and new‐onset AF group. Cardiac MRI images were analyzed using cardiovascular imaging software CVI 42 (Circle Cardiovascular Imaging, Canada). An automatic tracing algorithm was applied to obtain strain values. The reservoir strain, conduit strain, and booster strain were included in model 1, model 2, and model 3, respectively. Statistical Tests: Student's t‐test, Mann–Whiney U test, and chi‐square test were performed. Variables with a P ≤ 0.05 were incorporated into the logistic regression analysis. Area under curve of receiver operating characteristic was used to assess the ability of LA strain to identify new‐onset AF. Bayesian information criterion, Akaike information criterion, and C‐index were used to make comparisons between three models. P < 0.05 was considered statistically significant. Results: Three models were used to assess LA strain identification ability for new‐onset AF. After including multiple factors, right coronary artery (RCA), LVEF, and reservoir strain were still risk factors for new‐onset AF in model 1. In model 2, age, RCA, LVEF, and conduit strain were still risk factors for new‐onset AF. In model 3, RCA, LVEF, LVEDVi, and booster strain were still risk factors for new‐onset AF. Model 2 has a stronger identification ability than others. Data Conclusion: LA strain associated with new‐onset AF after STEMI. The model including conduit strain was the best‐fit one. Level of Evidence: 4 Technical Efficacy: Stage 3 [ABSTRACT FROM AUTHOR]

Rationale: Limited information exists about the epidemiology, outcomes, and predictors of weaning from mechanical ventilation in patients with spinal cord injury. Objectives: Our aim was to investigate predictors of weaning outcomes for patients with traumatic spinal cord injury (tSCI) and develop and validate a prognostic model and score for weaning success. Methods: This was a registry-based, multicentric cohort study including all adult patients with tSCI requiring mechanical ventilation (MV) and admitted to one of the intensive care units (ICUs) of the Trauma Registry at St. Michael’s Hospital (Toronto, ON, Canada) and the Canadian Rick Hansen Spinal Cord Injury Registry between 2005 and 2019. The primary outcome was weaning success from MV at ICU discharge. Secondary outcomes included weaning success at Days 14 and 28, time to liberation from MV accounting for competing risk of death, and ventilator-free days at 28 and 60 days. Associations between baseline characteristics and weaning success or time to liberation from MV were measured using multivariable logistic and competing risk regressions. A parsimonious model to predict weaning success and ICU discharge was developed and validated via bootstrap. A prediction score for weaning success at ICU discharge was derived, and its discriminative ability was assessed using receiver operating characteristic curve analysis and compared with the Injury Severity Score (ISS). Results: Of 459 patients analyzed, 246 (53.6%), 302 (65.8%), and 331 (72.1%) were alive and free of MV at Day 14, Day 28, and ICU discharge, respectively; 54 (11.8%) died in the ICU. Median time to liberation from MV was 12 days. Factors associated with weaning success were Blunt injury (odds ratio [OR], 2.96; P = 0.010), ISS (OR, 0.98; P = 0.025), Complete syndrome (OR, 0.53; P = 0.009), age in Years (OR, 0.98; P = 0.003), and Cervical LEsion (OR, 0.60; P = 0.045). The BICYCLE score showed a greater area under the curve than the ISS (0.689 [95% confidence interval (CI), 0.631–0.743] vs. 0.537 [95% CI, 0.479–0.595]; P, 0.0001). Factors predicting weaning success also predicted time to liberation. Conclusions: In a large multicentric cohort, 72% of patients with tSCI were weaned and discharged alive from the ICU. Readily available admission characteristics can reasonably predict weaning success and help prognostication. [ABSTRACT FROM AUTHOR]

Objective To determine the extent of agreement between the original Central Sensitization Inventory (CSI) and the Rasch-calibrated version (RC-CSI) and to explore the association of both versions with psychophysical tests and their respective sensitivity and specificity. Methods Patients with knee osteoarthritis who were enrolled in a multicenter cohort study in Montreal, Canada, completed the original CSI, the RC-CSI, and psychophysical tests (i.e. pressure pain thresholds, temporal summation, conditioned pain modulation) according to standardized protocols. Bland-Altman analyses assessed the agreement between the original CSI and the RC-CSI; Spearman correlations and chi-squared analyses evaluated the association between the two CSI scores and the psychophysical tests. A CSI cut point of 40 and an RC-CSI cut point of 31.37 were used. Receiver operating characteristic curves and the resulting sensitivity and specificity with psychophysical tests were also analyzed. Results Two hundred ninety-three participants were included (58.7% female, mean age of 63.6 years, and body mass index 31.9 kg/m2). The original CSI and RC-CSI mean difference, 3.3/8.2, t (292) = 8.84 (P  < 0.001), was significantly different and indicated a small bias. Small but significant inverse correlations were found for the original CSI and RC-CSI scores with pressure pain thresholds at the forearm and patella, with variance explained ranging from 0.01 to 0.12. The largest area under the curve suggested cut points of 23 (CSI) and 25 (RC-CSI) with 80.9% sensitivity and 38.5% specificity. Conclusions Because of poor variance explained with psychophysical tests and high false positive rates, our results indicate that there is little clinical value of using either version of the CSI in people with knee osteoarthritis. [ABSTRACT FROM AUTHOR]

The aim of this study is to determine if frailty, defined as modified frailty index (MFI) >2.7, correlated with worse postoperative outcomes in patients with chronic subdural hematomas (CSDHs). We also compare the predictive ability of the MFI with another widely used frailty measure, the Clinical Frailty Scale (CFS). We conducted a retrospective chart review of elderly patients (≥65 years) who underwent a twist-drill craniostomy for the evacuation of CSDH at Hamilton General Hospital, Canada, between 2016 and 2018. The primary outcome was the modified Rankin Scale scores at discharge. Logistic regression analyses and receiver operating characteristic curves were carried out to further analyze the factors that influenced independence and functional improvement at discharge. Frail patients were significantly more dependent at discharge (P < 0.0001) and had a lower rate of functional improvement (P = 0.003). When compared with frailty measured by the MFI, frailty as measured by the CFS had a stronger association with functional independence (odds ratio [OR]: 0.081 [0.031, 0.211] vs. OR: 0.256 [0.124, 0.529]) and functional improvement (OR: 0.272 [0.106, 0.693] vs. OR: 0.406 [0.185,0.889]) on logistic regression analyses. Area under the receiver operating characteristic curve analyses showed that the inclusion of frailty into our predictive models improved accuracy. Elderly patients presenting with CSDH who are frail (MFI >0.27) have significantly worse functional outcomes following twist-drill craniostomies. Therefore assessing frailty in this population is important before managing these patients, and for this purpose the CFS is a superior predictor of postoperative function than the MFI. [ABSTRACT FROM AUTHOR]

The Bayes classification rule offers the optimal classifier, minimizing the classification error rate, whereas the Neyman–Pearson lemma offers the optimal family of classifiers to maximize the detection rate for any given false alarm rate. These motivate studies on comparing classifiers based on similarities between the classifiers and the optimal. In this article, we define partial order relations on classifiers and families of classifiers, based on rankings of rate function values and rankings of test function values, respectively. Each partial order relation provides a sufficient condition, which yields better classification error rates or better performance on the receiver operating characteristic analysis. Various examples and applications of the partial order theorems are discussed to provide comparisons of classifiers and families of classifiers, including the comparison of cross‐validation methods, training data that contains outliers, and labelling errors in training data. The Canadian Journal of Statistics 48: 152–166; 2020 © 2019 Statistical Society of Canada [ABSTRACT FROM AUTHOR]

Focused cardiac ultrasound (FoCUS) is becoming standard practice in a wide spectrum of clinical settings. There is limited data evaluating the real-world use of FoCUS with artificial intelligence (AI). Our objective was to determine the accuracy of FoCUS AI-assisted left ventricular ejection fraction (LVEF) assessment and compare its accuracy between novice and experienced users. In this prospective, multicentre study, participants requiring a transthoracic echocardiogram (TTE) were recruited to have a FoCUS done by a novice or experienced user. The AI-assisted device calculated LVEF at the bedside, which was subsequently compared to TTE. 449 participants were enrolled with 424 studies included in the final analysis. The overall intraclass coefficient was 0.904, and 0.921 in the novice (n = 208) and 0.845 in the experienced (n = 216) cohorts. There was a significant bias of 0.73% towards TTE (p = 0.005) with a level of agreement of 11.2%. Categorical grading of LVEF severity had excellent agreement to TTE (weighted kappa = 0.83). The area under the curve (AUC) was 0.98 for identifying an abnormal LVEF (<50%) with a sensitivity of 92.8%, specificity of 92.3%, negative predictive value (NPV) of 0.97 and a positive predictive value (PPV) of 0.83. In identifying severe dysfunction (<30%) the AUC was 0.99 with a sensitivity of 78.1%, specificity of 98.0%, NPV of 0.98 and PPV of 0.76. Here we report that FoCUS AI-assisted LVEF assessments provide highly reproducible LVEF estimations in comparison to formal TTE. This finding was consistent among senior and novice echocardiographers suggesting applicability in a variety of clinical settings. [ABSTRACT FROM AUTHOR]

Aims: This study aimed to determine the sensitivity and specificity of the National Institute of Neurological Disorders and Stroke (NINDS) and the Canadian Stroke Network (CSN) brief (5 min) screen composed of three items of the Montreal Cognitive Assessment (MoCA), in acute coronary syndrome (ACS) patients during hospital admission, relative to the full MoCA and potential alternative combinations of other items. Methods and results: Participants were consecutively recruited during ACS admission and administered the MoCA before discharge. The three NINDS–CSN screen items were extracted, collated and compared to the full MoCA. Receiver operator characteristic (ROC) curves were created to determine the sensitivity, specificity, and appropriate cut-off scores of the screens. The mean age of the sample (n = 81) was 63.49 [standard deviation (SD) 10.85] years and 49.4% screened positive for cognitive impairment. The NINDS–CSN mean score was 9.22 (SD 2.09 of the potential range 0–12). Area under the ROC (AUC) indicated high accuracy levels for screening for cognitive impairment (AUC = 0.89, P < 0.01, 95% confidence interval 0.82, 0.96) with none of the alternative combination screens performing better on both sensitivity and specificity. A cut-off score of ≤10 on the NINDS–CSN protocol provided 83% sensitivity and 80% specificity for classifying cognitive impairment. Conclusion: The NINDS–CSN protocol presents an accurate, feasible screen for cognitive impairment in patients following ACS for use at the bedside and potentially also for telephone screens. Diagnostic accuracy should be confirmed using a neurocognitive battery. Graphical Abstract [ABSTRACT FROM AUTHOR]

Background The Hospital Frailty Risk Score (HFRS) is scored using ICD-10 diagnostic codes in administrative hospital records. Home care clients in Canada are routinely assessed with Resident Assessment Instrument-Home Care (RAI-HC) which can calculate the Clinical Frailty Scale (CFS) and the Frailty Index (FI). Objective Measure the correlation between the HFRS, CFS and FI and compare prognostic utility for frailty-related outcomes. Design Retrospective cohort study. Setting Alberta, British Columbia and Ontario, Canada. Subjects Home care clients aged 65+ admitted to hospital within 180 days (median 65 days) of a RAI-HC assessment (n  = 167,316). Methods Correlation between the HFRS, CFS and FI was measured using the Spearman correlation coefficient. Prognostic utility of each measure was assessed by comparing measures of association, discrimination and calibration for mortality (30 days), prolonged hospital stay (10+ days), unplanned hospital readmission (30 days) and long-term care admission (1 year). Results The HFRS was weakly correlated with the FI (ρ 0.21) and CFS (ρ 0.28). Unlike the FI and CFS, the HFRS was unable to discriminate for 30-day mortality (area under the receiver operator characteristic curve (AUC) 0.506; confidence interval (CI) 0.502–0.511). It was the only measure that could discriminate for prolonged hospital stay (AUC 0.666; CI 0.661–0.673). The HFRS operated like the FI and CFI when predicting unplanned readmission (AUC 0.530 CI 0.526–0.536) and long-term care admission (AUC 0.600; CI 0.593–0.606). Conclusions The HFRS identifies a different subset of older adult home care clients as frail than the CFS and FI. It has prognostic utility for several frailty-related outcomes in this population, except short-term mortality. [ABSTRACT FROM AUTHOR]

Geological maps are powerful models for visualizing the complex distribution of rock types through space and time. However, the descriptive information that forms the basis for a preferred map interpretation is typically stored in geological map databases as unstructured text data that are difficult to use in practice. Herein we apply natural language processing (NLP) to geoscientific text data from Canada, the U.S., and Australia to address that knowledge gap. First, rock descriptions, geological ages, lithostratigraphic and lithodemic information, and other long-form text data are translated to numerical vectors, i.e., a word embedding, using a geoscience language model. Network analysis of word associations, nearest neighbors, and principal component analysis are then used to extract meaningful semantic relationships between rock types. We further demonstrate using simple Naive Bayes classifiers and the area under receiver operating characteristics plots (AUC) how word vectors can be used to: (1) predict the locations of "pegmatitic" (AUC = 0.962) and "alkalic" (AUC = 0.938) rocks; (2) predict mineral potential for Mississippi-Valley-type (AUC = 0.868) and clastic-dominated (AUC = 0.809) Zn-Pb deposits; and (3) search geoscientific text data for analogues of the giant Mount Isa clastic-dominated Zn-Pb deposit using the cosine similarities between word vectors. This form of semantic search is a promising NLP approach for assessing mineral potential with limited training data. Overall, the results highlight how geoscience language models and NLP can be used to extract new knowledge from unstructured text data and reduce the mineral exploration search space for critical raw materials. [ABSTRACT FROM AUTHOR]

Objectives Damage accrual in SSc can be tracked using the Scleroderma Clinical Trials Consortium Damage Index (DI). Our goal was to develop a prediction model for damage accrual in SSc patients with early disease. Methods Using patients with <2 years disease duration from Canada and Australia as a derivation cohort, and from the Netherlands as a validation cohort, we used group-based trajectory modelling (GBTM) to determine 'good' and 'bad' latent damage trajectories. We developed a prediction model from this analysis and applied it to patients from derivation and validation cohorts. We plotted the actual DI trajectories of the patients predicted to be in 'good' or 'bad' groups. Results We found that the actual trajectories of damage accumulation for lcSSc and dcSSc were very different, so we studied each subset separately. GBTM found two distinct trajectories in lcSSc and three in dcSSc. We collapsed the two worse trajectories in the dcSSc into one group and developed a prediction model for inclusion in either 'good' or 'bad' trajectories. The performance of models using only baseline DI and sex was excellent with ROC AUC of 0.9313 for lcSSc and 0.9027 for dcSSc. Using this model, we determined whether patients would fall into 'good' or 'bad' trajectory groups and then plotted their actual trajectories which showed clear differences between the predicted 'good' and 'bad' cases in both derivation and validation cohorts. Conclusions A simple model using only cutaneous subset, baseline DI and sex can predict damage accumulation in early SSc. [ABSTRACT FROM AUTHOR]

Background Parkinson's disease (PD) increases risk for dementia and cascading adverse outcomes. The eight-item Montreal Parkinson Risk of Dementia Scale (MoPaRDS) is a rapid, in-office dementia screening tool. We examine predictive validity and other characteristics of the MoPaRDS in a geriatric PD cohort by testing a series of alternative versions and modelling risk score change trajectories. Methods Participants were 48 initially non-demented PD patients (Mage = 71.6 years, range = 65--84) from a three-year, threewave prospective Canadian cohort study. A dementia diagnosis at Wave 3 was used to stratify two baseline groups: PD with Incipient Dementia (PDID) and PD with No Dementia (PDND). We aimed to predict dementia three years prior to diagnosis using baseline data for eight indicators that harmonized with the original report, plus education. Results Three MoPaRDS items (age, orthostatic hypotension, mild cognitive impairment [MCI]) discriminated the groups both independently and as a composite three-item scale (area under the curve [AUC] = 0.88). The eight-item MoPaRDS reliably discriminated PDID from PDND (AUC = 0.81). Education did not improve predictive validity (AUC = 0.77). Performance of the eight-item MoPaRDS varied across sex (AUCfemales = 0.91; AUCmales = 0.74), whereas the three-item configuration did not (AUCfemales = 0.88; AUCmales = 0.91). Risk scores of both configurations increased over time. Conclusions We report new data on the application of the MoPaRDS as a dementia prediction tool for a geriatric PD cohort. Results support the viability of the full MoPaRDS, and indicate that an empirically determined brief version is a promising complement. [ABSTRACT FROM AUTHOR]

Background: The goal of the Edmonton Classification System for Cancer Pain (ECS-CP) is to create an international classification system for cancer pain. Previous studies reinforce the need for standardized training to ensure consistency across assessors. There is no universally accepted classification for neuropathic pain. Objectives: Our primary objective was to describe the prevalence of ECS-CP features in a diverse sample of advanced cancer patients, using assessors with standardized training. The secondary objectives were to: (1) determine the prevalence of neuropathic pain using the Neuropathic Pain Special Interest Group (NeuPSIG) criteria and (2) examine the relationship between specific predictors: ECS-CP features, age, Palliative Performance Scale, Morphine Equivalent Daily Dose (MEDD), setting, and pain intensity; and neuropathic pain. Methods: A total of 1050 adult patients with advanced cancer were recruited from 11 Canadian sites. A clinician completed the ECS-CP and NeuPSIG criteria, and collected additional information including demographics and pain intensity (now). All assessors received standardized training. Results: Of 1050 evaluable patients, 910 (87%) had cancer pain: nociceptive (n = 626; 68.8%); neuropathic (n = 227; 24.9%); incident (n = 329; 36.2%); psychological distress (n = 209; 23%); addictive behavior (n = 51; 5.6%); and normal cognition (n = 639; 70.2%). The frequencies of ECS-CP features and pain intensity scores varied across sites and settings, with more acute settings having higher frequencies of complex pain features. The overall frequency of neuropathic pain was 24.9%, ranging from 11% (hospices) to 34.2% (palliative outpatient clinic) across settings. Multivariate logistic regression analysis revealed that age <60 years, MEDD ≥19 mg, pain intensity ≥7/10, and incident pain were significant independent predictors of neuropathic pain (p < 0.05). Conclusion: The ECS-CP was able to detect salient pain features across settings. Furthermore, the frequencies of neuropathic pain utilizing the NeuPSIG criteria fits within the lower-end of literature estimates (13%–40%). Further research is warranted to validate the NeuPSIG criteria in cancer pain. [ABSTRACT FROM AUTHOR]

Both genetic variants and maternal blood mRNA levels of EBF1 gene have been linked to sPTB. Animal and human studies suggest that specific EBF1-based miRNAs are involved in various physiological and pathophysiological processes. However, to date, we did not find any reports of EBF1-based miRNAs or miRNA transcripts in relation to sPTB. We therefore aimed to examine whether maternal blood early B cell factor 1 (EBF1) gene-based microRNA (miRNA) transcripts can be used for detecting risk of spontaneous preterm birth (sPTB). We conducted a nested case-control study within a Canadian cohort consisting of 1878 singleton pregnancies enrolled from May 2008 to December 2010 in Calgary, Alberta, Canada. We used a public gene expression dataset (GSE59491) derived from maternal blood in trimesters 2–3 that included women with sPTB (n = 51) and term births (n = 106) matched for maternal age, race/ethnicity, pre-pregnancy body mass index, smoking during pregnancy, and parity within the Canadian cohort. Two bioinformatics tools, miRWalk and STarMirDB, with different algorithms were applied to retrieve miRNA transcripts that putatively target the EBF1 gene (i.e. EBF1-based). Limma moderated t-tests were used to examine differentially expressed (DE) miRNA transcripts (sPTB vs term) within trimesters. Logistic regression models with miRNA transcript tertiles were applied to assess threshold associations between candidate miRNA transcripts' levels and sPTB. Receiver operating characteristic (ROC) analyses were used to identify the maximum Youden Index and its corresponding optimal sensitivity/specificity cut-point of EBF1-based miRNA transcripts for classifying sPTB, and to compare the classification performance of a linear combination (score) of miRNA transcripts with that of individual miRNA transcripts. A five-fold cross-validation was applied to examine the possible overfitting problem of the final ROC model. Four maternal blood EBF1-based miRNA transcripts (MIR4266, MIR1251, MIR601, MIR3612) in the 3rd trimester were significantly associated with sPTB. The odds ratios (95%CIs) for highest versus lowest tertile of the four miRNA transcripts were 3.01–5.25(1.21–13.14, p ≤.018). The combined 4-miRNA transcripts' score significantly improved the classification of sPTB compared to individual miRNA transcripts (AUC increased from 0.65–0.69 to 0.82, p ≤.0034) and showed a sensitivity for sPTB of 0.81 and a specificity of 0.72. The final ROC model of the EBF1-based 4 miRNA transcripts' score in cases and controls had no significant overfitting issue. Maternal blood EBF1-based miRNA transcripts may, along with other biomarkers, be useful in screening for sPTB risk in 3rd trimester. Our results also provide clues for further study of potential molecular mechanisms underlying the relationship between EBF1 gene and sPTB, e.g. connecting genetic variants, mRNA expression, and miRNA regulation. [ABSTRACT FROM AUTHOR]

Study Question: Can we derive adequate models to predict the probability of conception among couples actively trying to conceive?Summary Answer: Leveraging data collected from female participants in a North American preconception cohort study, we developed models to predict pregnancy with performance of ∼70% in the area under the receiver operating characteristic curve (AUC).What Is Known Already: Earlier work has focused primarily on identifying individual risk factors for infertility. Several predictive models have been developed in subfertile populations, with relatively low discrimination (AUC: 59-64%).Study Design, Size, Duration: Study participants were female, aged 21-45 years, residents of the USA or Canada, not using fertility treatment, and actively trying to conceive at enrollment (2013-2019). Participants completed a baseline questionnaire at enrollment and follow-up questionnaires every 2 months for up to 12 months or until conception. We used data from 4133 participants with no more than one menstrual cycle of pregnancy attempt at study entry.Participants/materials, Setting, Methods: On the baseline questionnaire, participants reported data on sociodemographic factors, lifestyle and behavioral factors, diet quality, medical history and selected male partner characteristics. A total of 163 predictors were considered in this study. We implemented regularized logistic regression, support vector machines, neural networks and gradient boosted decision trees to derive models predicting the probability of pregnancy: (i) within fewer than 12 menstrual cycles of pregnancy attempt time (Model I), and (ii) within 6 menstrual cycles of pregnancy attempt time (Model II). Cox models were used to predict the probability of pregnancy within each menstrual cycle for up to 12 cycles of follow-up (Model III). We assessed model performance using the AUC and the weighted-F1 score for Models I and II, and the concordance index for Model III.Main Results and the Role Of Chance: Model I and II AUCs were 70% and 66%, respectively, in parsimonious models, and the concordance index for Model III was 63%. The predictors that were positively associated with pregnancy in all models were: having previously breastfed an infant and using multivitamins or folic acid supplements. The predictors that were inversely associated with pregnancy in all models were: female age, female BMI and history of infertility. Among nulligravid women with no history of infertility, the most important predictors were: female age, female BMI, male BMI, use of a fertility app, attempt time at study entry and perceived stress.Limitations, Reasons For Caution: Reliance on self-reported predictor data could have introduced misclassification, which would likely be non-differential with respect to the pregnancy outcome given the prospective design. In addition, we cannot be certain that all relevant predictor variables were considered. Finally, though we validated the models using split-sample replication techniques, we did not conduct an external validation study.Wider Implications Of the Findings: Given a wide range of predictor data, machine learning algorithms can be leveraged to analyze epidemiologic data and predict the probability of conception with discrimination that exceeds earlier work.Study Funding/competing Interest(s): The research was partially supported by the U.S. National Science Foundation (under grants DMS-1664644, CNS-1645681 and IIS-1914792) and the National Institutes for Health (under grants R01 GM135930 and UL54 TR004130). In the last 3 years, L.A.W. has received in-kind donations for primary data collection in PRESTO from FertilityFriend.com, Kindara.com, Sandstone Diagnostics and Swiss Precision Diagnostics. L.A.W. also serves as a fibroid consultant to AbbVie, Inc. The other authors declare no competing interests.Trial Registration Number: N/A. [ABSTRACT FROM AUTHOR]

Deciding when to protect threatened species habitat when complete knowledge about the habitat extent is uncertain is a common problem in conservation. More accurate habitat mapping improves conservation outcomes once that habitat is protected. However, delaying protection to improve accuracy can lead to species decline or, at worst, local extinction when threats to that habitat continue unabated before protection is implemented. Hence, there is a trade‐off between gaining knowledge and taking conservation action.We quantified this trade‐off and determined the optimal time to spend learning about a species' habitat before protecting that habitat. We used a range of hypothetical learning curves to model improvements in the accuracy of predicted habitat over time, and receiver operating characteristic (ROC) curves to model the corresponding increase in the proportion of habitat protected. We used rates of habitat loss to model the impact of delaying habitat protection and derived analytical solutions to the problem for different types of learning curves.We illustrate our approach using two threatened species, the koala Phascolarctos cinereus in Australia and northern abalone Haliotis kamtschatkana in Canada. Our approach confirms that when impacts of threatening processes are incurred rapidly, the need for timely protection is high, and the optimal time to spend learning is short for all learning curves. When the rate of habitat loss is low, we benefit from better habitat identification, and the optimal time to protect is sensitive to assumptions about how we learn and the proportion of non‐habitat we are willing to protect unnecessarily.Navigating the trade‐off between information gain and timely action is a common problem in conservation. By optimizing the trade‐off between the benefits of improving mapping accuracy and the costs of delaying protection, we provide guidelines on the effective allocation of resources between habitat identification and habitat protection. Importantly, by explicitly modelling this trade‐off with a range of learning curves and estimates of the rates of habitat loss or other threatening processes, we can predict the optimal time to spend learning even when relatively little is known about a species and its habitat. [ABSTRACT FROM AUTHOR]

Study Design: An observational prospective study.Objective: The study objective was to assess the reliability and validity of the French-Canadian version (FCSSSQ) of the Swiss Spinal Stenosis questionnaire (SSSQ).Summary Of Background Data: The SSSQ is a validated disease-specific tool developed to assess pain, function, and satisfaction with care in patients with lumbar spinal stenosis (LSS).Methods: The French-Canadian translation and adaptation of the SSSQ was completed following a four-stage approach: (1) forward translation; (2) synthesis; (3) expert committee review; and (4) testing of the questionnaire prefinal version. Psychometric properties were subsequently determined in patients who had undergone decompressive surgery for degenerative LSS. Patients completed the SSSQ adapted version, the Numerical Rating Scale, and the Oswestry Disability Index. Test-retest reliability was assessed with weighted Kappa score and intraclass correlation coefficient. Construct validity was evaluated using the Spearman correlation coefficient and responsiveness with the mean standardized response as well as the area under the receiver operating characteristic curve (AUROC). Floor and ceiling effects and internal consistency were also evaluated.Results: Twenty-five and 50 patients were included for the adaptation and validation processes, respectively. The test-retest reliability of the FCSSSQ total score was excellent [intraclass correlation coefficient (ICC) = 0.996; 95% confidence interval (95% CI) 0.938-0.982]. High Spearman correlation coefficients were also found between the total FCSSSQ score and the leg pain Numerical Rating Scale (NRS, 0.801), back pain NRS (0.705), and the Oswestry Disability Index (ODI, 0.825) scores. External responsiveness analysis was satisfactory with an AUROC of 0.875 (95% CI 0.744-1.0). The lowest FCSSSQ possible score was observed in five patients (10%), whereas no patients reported the highest possible score. The Cronbach α coefficient ranged from 0.810 to 0.945 indicating good to excellent internal consistency.Conclusion: The French-Canadian version of the SSSQ is a reliable and valid questionnaire consistent with the original English version. This new version will help French-speaking clinicians and scientists document changes in condition and treatment satisfaction in patients with LSS.Level Of Evidence: 2. [ABSTRACT FROM AUTHOR]

Objectives Imaging modalities have become common in evaluating patients for a possible diagnosis of GCA. This study seeks to contextualize how temporal arterial magnetic resonance angiography (TA-MRA) can be used in facilitating the diagnosis of GCA. Methods A retrospective cohort study was performed on patients who had been previously referred to a rheumatologist for evaluation of possible GCA in Hamilton, Ontario, Canada. Data including clinical features, inflammatory markers, imaging, and biopsy results were extracted. Multivariable logistic regression model to predict the diagnosis of GCA. Using these models, the utility of TA-MRA in series with or in parallel to clinical evaluation was demonstrated across the cohort as well as in subgroups defined by biopsy and imaging status. Results In total 268 patients had complete data. Those diagnosed with biopsy- and/or imaging-positive GCA were more likely to demonstrate classic features including jaw claudication and vision loss. Clinical multivariable modelling allowed for fair discriminability [receiver operating characteristic (ROC) 0.759, 95% CI: 0.703, 0.815] for diagnosing GCA; there was excellent discriminability in facilitating the diagnosis of biopsy-positive GCA (ROC 0.949, 0.898–1.000). When used in those with a pre-test probability of 50% or higher, TA-MRA had a positive predictive value of 93.0%; in those with a pre-test probability of 25% or less TA-MRA had a negative predictive value of 89.5%. Conclusion In those with high disease probability, TA-MRA can effectively rule in disease (and replace temporal artery biopsy). In those with low to medium probability, TA-MRA can help rule out the disease, but this continues to be a challenging diagnostic population. [ABSTRACT FROM AUTHOR]

Background: AAPM Task Group (TG) 275 was charged with developing practical, evidence‐based recommendations for physics plan and chart review clinical processes for radiation therapy. As part of this charge, and to characterize practices and clinical processes, a survey of the medical physics community was developed and conducted. Detailed analyses and trends based on the survey that exceeded TG report length constraints are presented herein. Aims: The design, development, and detailed results of the TG‐ 275 survey as well as statistical analysis and trends are described in detail. This is complementary material to the TG 275 report. Methods and materials: The survey consisted of 100 multiple‐choice questions divided into four main sections: 1) Demographics, 2) Initial Plan Check, 3) On‐Treatment, and 4) End‐of‐Treatment Chart Check. The survey was released to all AAPM members who self‐reported working in the radiation oncology field, and it was kept open for 7 weeks. Results were summarized using descriptive statistics. To study practice differences, tests of association were performed using data grouped by four demographic questions: 1) Institution Type, 2) Average number of patients treated daily, 3) Radiation Oncology Electronic Medical Record, and 4) Perceived Culture of Safety. Results: The survey captured 1370 non‐duplicate entries from the United States and Canada. Differences across practices were grouped and presented based on Process‐Based and Check‐Specific questions. A risk‐based summary was created to show differences amongst the four demographic questions for checks associated with the highest risk failure modes identified by TG‐275. Conclusion: The TG‐275 survey captured a baseline of practices on initial plan, on‐treatment, and end‐of‐treatment checks across a wide variety of clinics and institutions. The results of test of association showed practice heterogeneities as a function of demographic characteristics. Survey data were successfully used to inform TG‐275 recommendations. [ABSTRACT FROM AUTHOR]

Many gamblers would prefer to reduce gambling on their own rather than to adopt an abstinence approach within the context of a gambling treatment program. Yet responsible gambling guidelines lack quantifiable markers to guide gamblers in wagering safely. To address these issues, the current investigation implemented receiver operating characteristic (ROC) analysis to identify behavioral indicators of harmful and problem gambling. Gambling involvement was assessed in 503 participants (275 psychiatric outpatients and 228 community gamblers) with the Canadian Problem Gambling Index. Overall gambling frequency, duration, and expenditure were able to distinguish harmful and problematic gambling at a moderate level. Indicators of harmful gambling were generated for engagement in specific gambling activities: frequency of tickets and casino; duration of bingo, casino, and investments; and expenditures on bingo, casino, sports betting, games of skill, and investments. Indicators of problem gambling were similarly produced for frequency of tickets and casino, and expenditures on bingo, casino, games of skill, and investments. Logistic regression analyses revealed that overall gambling frequency uniquely predicted the presence of harmful and problem gambling. Furthermore, frequency indicators for tickets and casino uniquely predicted the presence of both harmful and problem gambling. Together, these findings contribute to the development of an empirically based method enabling the minimization of harmful or problem gambling through self-control rather than abstinence.

Background Restless legs syndrome (RLS) is common in end-stage renal disease and is associated with reduced health-related quality of life. Simple and accurate screening instruments are needed since RLS is underdiagnosed and treatable. We examined the operating characteristics of screening questions and a disease-specific measurement tool for the diagnosis of RLS in hemodialysis. Methods We conducted a cohort study of prevalent adult hemodialysis patients in Hamilton, Canada. The diagnosis of RLS was made using the 2012 Revised International Restless Legs Syndrome Study Group (IRLSSG) criteria. All participants received three screening instruments: (i) a single screening question for RLS derived from a nondialysis population; (ii) a single question from the Edmonton Symptom Assessment System (ESAS); and (iii) the IRLSSG Rating Scale (IRLS). All instruments were compared with the reference standard using logistic regression from which receiver operating characteristics curves were generated. Cutoffs associated with maximum performance were identified. Results We recruited 50 participants with a mean (SD) age of 64 (12.4) years, of whom 52% were male and 92% were on three times weekly hemodialysis. Using the reference standard, 14 (28%) had a diagnosis of RLS. The single screening question for RLS had an area under the receiver operating curve (AUROC) of 0.72 with a sensitivity of 85.7% and specificity of 58.3%. An ESAS cutoff of ≥1 had the highest AUROC at 0.65 with a sensitivity of 79% and specificity of 56%. An IRLS cutoff of ≥20 had the highest AUROC at 0.75 with a sensitivity of 71% and specificity of 81%. Conclusion IRLS had better specificity than the single question or ESAS for the diagnosis of RLS. [ABSTRACT FROM AUTHOR]

BACKGROUND: Diaphragmatic respiratory effort during mechanical ventilation is an important determinant of patient outcome, but direct measurement of diaphragmatic contractility requires specialized instrumentation and technical expertise. We sought to determine whether routinely collected clinical variables can predict diaphragmatic contractility and stratify the risk of diaphragm atrophy. METHODS: We conducted a secondary analysis of a prospective cohort study on diaphragm ultrasound in mechanically ventilated subjects. Clinical variables, such as breathing frequency, ventilator settings, and blood gases, were recorded longitudinally. Machine learning techniques were used to identify variables predicting diaphragm contractility and stratifying the risk of diaphragm atrophy (> 10% decrease in thickness from baseline). Performance of the variables was evaluated in mixed-effects logistic regression and random-effects tree models using the area under the receiver operating characteristic curve. RESULTS: Measurements were available for 761 study days in 191 subjects, of whom 73 (38%) developed diaphragm atrophy. No routinely collected clinical variable, alone or in combination, could accurately predict either diaphragm contractility or the development of diaphragm atrophy (model area under the receiver operating characteristic curve 0.63--0.75). The risk of diaphragm atrophy was not significantly different according to the pres- ence or absence of patient-triggered breaths (38.3% vs 38.6%; odds ratio 1.01, 95% CI 0.05--2.03). Diaphragm thickening fraction < 15% during either of the first 2 d of the study was associated with a higher risk of atrophy (44.6% vs 26.1%; odds ratio 2.28, 95% CI 1.05--4.95). CONCLUSIONS: Diaphragmatic contractility and the risk of diaphragm atrophy could not be reliably determined from routinely collected clinical variables and ventilator settings. A single measurement of dia- phragm thickening fraction measured within 48 h of initiating mechanical ventilation can be used to stratify the risk of diaphragm atrophy during mechanical ventilation. [ABSTRACT FROM AUTHOR]

Background Guidelines for fall prevention in older adults recommend mobility screening for fall risk assessment; however, there is no consensus on which test to use and at what cutoff. This study aimed to determine the accuracy and optimal cut-off values of commonly used mobility tests for predicting falls in the Canadian Longitudinal Study on Aging (CLSA). Methods Mobility tests at baseline included the Timed Up and Go (TUG), Single Leg Stance (SLS), chair-rise and gait speed. Inclusion criteria were: age ≥ 65 years and meeting first-level fall screening criteria (i.e. history of a fall or mobility problem) at baseline. Accuracy of fall prediction at 18-months for each test was measured by the area under the receiver operating curve (AUC). Results Of 1,121 participants that met inclusion criteria (mean age 75.2 ± 5.9 years; 66.6% women), 218 (19.4%) reported ≥one fall at 18 months. None of the tests achieved acceptable accuracy for identifying individuals with ≥one fall at follow-up. Among women 65–74 and 75–85 years, the TUG identified recurrent fallers (≥two falls) with optimal cut-off scores of 14.1 and 12.9 s (both AUCs 0.70), respectively. Among men 65–74 years, only the SLS showed acceptable accuracy (AUC 0.85) for identifying recurrent fallers with an optimal cutoff of 3.6 s. Conclusions Our findings indicate that commonly used mobility tests do not have sufficient discriminability to identify fallers in a population-based sample of community-dwelling older adults. The TUG and SLS can identify recurrent fallers; however, their accuracy and cut-off values vary by age and sex. [ABSTRACT FROM AUTHOR]

Background: Canada is an ethnically-diverse country, yet its lack of ethnicity information in many large databases impedes effective population research and interventions. Automated ethnicity classification using machine learning has shown potential to address this data gap but its performance in Canada is largely unknown. This study conducted a large-scale machine learning framework to predict ethnicity using a novel set of name and census location features. Methods: Using census 1901, the multiclass and binary class classification machine learning pipelines were developed. The 13 ethnic categories examined were Aboriginal (First Nations, Métis, Inuit, and all-combined)), Chinese, English, French, Irish, Italian, Japanese, Russian, Scottish, and others. Machine learning algorithms included regularized logistic regression, C-support vector, and naïve Bayes classifiers. Name features consisted of the entire name string, substrings, double-metaphones, and various name-entity patterns, while location features consisted of the entire location string and substrings of province, district, and subdistrict. Predictive performance metrics included sensitivity, specificity, positive predictive value, negative predictive value, F1, Area Under the Curve for Receiver Operating Characteristic curve, and accuracy. Results: The census had 4,812,958 unique individuals. For multiclass classification, the highest performance achieved was 76% F1 and 91% accuracy. For binary classifications for Chinese, French, Italian, Japanese, Russian, and others, the F1 ranged 68–95% (median 87%). The lower performance for English, Irish, and Scottish (F1 ranged 63–67%) was likely due to their shared cultural and linguistic heritage. Adding census location features to the name-based models strongly improved the prediction in Aboriginal classification (F1 increased from 50% to 84%). Conclusions: The automated machine learning approach using only name and census location features can predict the ethnicity of Canadians with varying performance by specific ethnic categories. [ABSTRACT FROM AUTHOR]

Aims: Concomitant cardiac amyloidosis (CA) in severe aortic stenosis (AS) is difficult to recognize, since both conditions are associated with concentric left ventricular thickening. We aimed to assess type, frequency, screening parameters, and prognostic implications of CA in AS.Methods and Results: A total of 191 consecutive AS patients (81.2 ± 7.4 years; 50.3% female) scheduled for transcatheter aortic valve replacement (TAVR) were prospectively enrolled. Overall, 81.7% underwent complete assessment including echocardiography with strain analysis, electrocardiography (ECG), cardiac magnetic resonance imaging (CMR), 99m Tc-DPD scintigraphy, serum and urine free light chain measurement, and myocardial biopsy in immunoglobulin light chain (AL)-CA. Voltage/mass ratio (VMR; Sokolow-Lyon index on ECG/left ventricular mass index) and stroke volume index (SVi) were tested as screening parameters. Receiver operating characteristic curve, binary logistic regression, and Kaplan-Meier curve analyses were performed. CA was found in 8.4% of patients (n = 16); 15 had transthyretin (TTR)-CA and one AL-CA. While global longitudinal strain by echo did not reliably differentiate AS from CA-AS [area under the curve (AUC) 0.643], VMR as well as SVi showed good discriminative power (AUC 0.770 and 0.773, respectively), which was comparable to extracellular volume by CMR (AUC 0.756). Also, VMR and SVi were independently associated with CA by multivariate logistic regression analysis (P = 0.016 and P = 0.027, respectively). CA did not significantly affect survival 15.3 ± 7.9 months after TAVR (P = 0.972).Conclusion: Both TTR- and AL-CA can accompany severe AS. Parameters solely based on ECG and echocardiography allow for the identification of the majority of CA-AS. In the present cohort, CA did not significantly worsen prognosis 15.3 months after TAVR. [ABSTRACT FROM AUTHOR]

Objective: To investigate the diagnostic values of T2 mapping and diffusion-weighted imaging (DWI) for active sacroiliitis in ankylosing spondylitis (AS) and to evaluate the correlations of T2 and ADC values with Bath Ankylosing Spondylitis Disease Activity Index (BASDAI) and Spondyloarthritis Research Consortium of Canada (SPARCC) scores.Methods: A total of 77 AS patients with sacroiliitis and 45 healthy controls were enrolled. All patients were scanned by standard magnetic resonance imaging longitudinal relaxation time (T1)-weighted imaging (T1WI), fat-saturated T2-weighted imaging (FS-T2WI)] and DWI, and T2 mapping of the sacroiliac joints. According to whether subchondral bone marrow edema was present in the FS-T2WI sequence, the 77 patients were divided into an active group (41 cases) and an inactive group (36 cases). The T2 and apparent diffusion coefficient (ADC) values of the subchondral bone marrow were measured in the active group, the inactive group, and the healthy control group. The average T2 and ADC values were compared among the three groups. Receiver operating characteristic (ROC) curves were used to analyze the diagnostic efficacy of T2 and ADC values for sacroiliitis. The correlations of T2 and ADC values with the BASDAI score and the SPARCC score were analyzed.Results: The T2 and ADC values in the active group were higher than those in the inactive group, while that in the inactive group were significantly higher than those in the healthy control group (p < 0.0001). The T2 and ADC values of the AS patients were positively correlated with BASDAI scores, and the correlation coefficients (r) were 0.786 (p < 0.0001) and 0.842 (p < 0.0001), respectively. The areas under the ROC curves (AUCs) of T2 and ADC values between the active and inactive groups, the active group and the healthy control group, and the inactive group and the healthy control group were 0.889 (95% CI, 0.80-0.95) and 0.917 (95% CI, 0.83-0.97), 0.982 (95% CI, 0.93-1.00) and 0.984 (95% CI, 0.93-1.00), and 0.628 (95% CI, 0.51-0.73) and 0.871 (95% CI, 0.78-0.94), respectively. The T2 and ADC values of the AS patients in the active group were positively correlated with SPARCC scores, and the correlation coefficients (r) were 0.757 (p < 0.0001) and 0.764 (p < 0.0001), respectively.Conclusion: T2 and ADC values can be used to quantitatively assess the activity of AS, and the efficacy of the ADC value in the diagnosis of AS was higher than that of the T2 value. [ABSTRACT FROM AUTHOR]

Objective. To determine if osteosarcoma (OS) and Ewing sarcoma (EWS) of the pelvis based on MRI can be differentiated using radiomic analysis. Materials and Methods. In this study, 3.0 T magnetic resonance (MR) data of 66 patients (40 males and 26 females, mean age 27.6 ± 13.9 years) with pathologically confirmed OS or EWS of the pelvis (35 with OS and 31 with EWS) taken from April 2013 to December 2017 were retrospectively reviewed. T2-weighted fat-saturated (T2-FS) and contrast-enhanced T1-weighted (CET1) images were manually segmented, and imaging features were extracted. Independent-sample t -test, Spearman's test, and the least absolute shrinkage and selection operator (LASSO) method were used to select the most useful features from the original data set. The performance of radiomic analysis was investigated by the area under the receiver operating characteristic (ROC) curve (AUC) analysis. Results. 385 initial features were extracted from T2-FS and CET1 MR data. Nine features from T2-FS and 7 features from CET1 were selected by using the LASSO method. The radiomic analysis to differentiate OS and EWS of the pelvis based on T2-FS and CET1 images using the aforementioned selected features achieved AUC values of 0.881 (95% confidence interval (CI): 0.799–0.963) and 0.765 (95% CI: 0.652–0.878), respectively. Conclusion. Radiomic analysis showed potential in differentiating OS from EWS of the pelvis, in which T2-FS demonstrated better diagnostic value. To differentiate OS from EWS of the pelvis using our multiparametric MRI-based radiomic analysis could preoperatively improve diagnostic accuracy and greatly contribute to therapy planning. [ABSTRACT FROM AUTHOR]

Objective: The Psoriatic Arthritis Disease Activity Score (PASDAS) is a composite disease activity measure (range 0-10) for psoriatic arthritis (PsA). We aimed to validate a cutoff value of PASDAS that defines minimal disease activity (MDA) state, as well as to validate previously defined PASDAS cutoffs for low and high disease activity.Methods: Patients were prospectively recruited from the University of Toronto PsA clinic according to a standard protocol, and variables necessary to complete the PASDAS and the MDA were collected. Receiver-operating characteristic (ROC) curve analysis determined the optimal PASDAS cutoff discriminating patients in MDA state from those not in MDA. Previously proposed PASDAS disease activity cutoff scores were validated by determining the proportion of patients requiring treatment escalation, a surrogate of active disease, in each of low, moderate, and high disease activity groups.Results: One hundred seventy-eight patients [53.9% male, mean PASDAS 3.29 (SD 1.29), 47.8% in MDA] were recruited. ROC curve analysis identified a PASDAS score of 3.2 as the point that maximized the sensitivity and specificity for MDA based on 5 of 7 criteria (sensitivity 88%, specificity 92%, area under the curve 0.96). For MDA based on meeting 6 of 7 and 7 of 7 criteria, PASDAS scores of 2.6 and 2.1 maximized sensitivity and specificity, respectively. An increasing proportion of patients from low to moderate to high disease activity groups required treatment escalation, increasing from 8.1% to 42% to 67%, respectively.Conclusion: A PASDAS score < 3.2 reflects MDA. This study has externally validated PASDAS cutoff scores previously proposed to differentiate between low, moderate, and high disease activity. [ABSTRACT FROM AUTHOR]

Objectives: This study compares the behavioral profile of children with fetal alcohol spectrum disorder (FASD) who were diagnosed using the Canadian Guidelines with children with prenatal alcohol exposure who did not meet criteria for a FASD diagnosis., Methods and Procedures: To accomplish this, we used caregiver and teacher questionnaires evaluating different aspects of behavior. Investigated were 170 children, 109 who received a diagnosis of FASD (Diagnosed Group) and 61 who did not (Non-Diagnosed Group). On the caregiver report, children in the Diagnosed Group had more internalizing and externalizing problems on the CBCL, more executive function difficulties on the BRIEF and more attention problems on the Conner's Rating Scale, compared to the Non-Diagnosed Group. On teacher report, children in the Diagnosed Group had more internalizing and externalizing problems on the TRF and more attention problems on the Conner's Rating Scale, compared to the Non-Diagnosed Group. For both informants, more children in the Diagnosed group had scores in the clinically elevated range., Conclusion: Overall, the present results identify key caregiver- and teacher-rated profiles of children with FASD diagnoses. These profiles will aid in better understanding, diagnosing and providing focused treatment approaches for children with FASD.