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BACKGROUND: Validating new algorithms, such as methods to disentangle intrinsic treatment risk from risk associated with experiential learning of novel treatments, often requires knowing the ground truth for data characteristics under investigation. Since the ground truth is inaccessible in real world data, simulation studies using synthetic datasets that mimic complex clinical environments are essential. We describe and evaluate a generalizable framework for injecting hierarchical learning effects within a robust data generation process that incorporates the magnitude of intrinsic risk and accounts for known critical elements in clinical data relationships. METHODS: We present a multi-step data generating process with customizable options and flexible modules to support a variety of simulation requirements. Synthetic patients with nonlinear and correlated features are assigned to provider and institution case series. The probability of treatment and outcome assignment are associated with patient features based on user definitions. Risk due to experiential learning by providers and/or institutions when novel treatments are introduced is injected at various speeds and magnitudes. To further reflect real-world complexity, users can request missing values and omitted variables. We illustrate an implementation of our method in a case study using MIMIC-III data for reference patient feature distributions. RESULTS: Realized data characteristics in the simulated data reflected specified values. Apparent deviations in treatment effects and feature distributions, though not statistically significant, were most common in small datasets (n

This research reports an exploratory study of 22 pre-service teachers involved in the Religious Education courses offered by The University of Notre Dame Australia, as part of the university's accreditation to teach religious education. The study examined the influence of key factors on how confident these pre-service teachers perceived themselves to be to engage with the necessary knowledge to effectively teach religious education in a Catholic school. The pre-service teachers were surveyed pre and post their final-year internship, and subsequently, six of these were interviewed at the end of their first term of employment. Results indicated that out of the five compulsory courses, the two teaching method courses were found to be most influential in developing confidence to teach. Family, religious background, schooling, and mentoring were found to be influential in confidence development. In addition, confidence improved across the timeframe.

BackgroundEarly hospital readmission for patients with cirrhosis continues to challenge the healthcare system. Risk stratification may help tailor resources, but existing models were designed using small, single-institution cohorts or had modest performance.AimsWe leveraged a large clinical database from the Department of Veterans Affairs (VA) to design a readmission risk model for patients hospitalized with cirrhosis. Additionally, we analyzed potentially modifiable or unexplored readmission risk factors.MethodsA national VA retrospective cohort of patients with a history of cirrhosis hospitalized for any reason from January 1, 2006, to November 30, 2013, was developed from 123 centers. Using 174 candidate variables within demographics, laboratory results, vital signs, medications, diagnoses and procedures, and healthcare utilization, we built a 47-variable penalized logistic regression model with the outcome of all-cause 30-day readmission. We excluded patients who left against medical advice, transferred to a non-VA facility, or if the hospital length of stay was greater than 30 days. We evaluated calibration and discrimination across variable volume and compared the performance to recalibrated preexisting risk models for readmission.ResultsWe analyzed 67,749 patients and 179,298 index hospitalizations. The 30-day readmission rate was 23%. Ascites was the most common cirrhosis-related cause of index hospitalization and readmission. The AUC of the model was 0.670 compared to existing models (0.649, 0.566, 0.577). The Brier score of 0.165 showed good calibration.ConclusionOur model achieved better discrimination and calibration compared to existing models, even after local recalibration. Assessment of calibration by variable parsimony revealed performance improvements for increasing variable inclusion well beyond those detectable for discrimination.

OBJECTIVE: Cirrhotic patients are at high hospitalisation risk with subsequent high mortality. Current risk prediction models have varied performances with methodological room for improvement. We used current analytical techniques using automatically extractable variables from the electronic health record (EHR) to develop and validate a posthospitalisation mortality risk score for cirrhotic patients and compared performance with the model for end-stage liver disease (MELD), model for end-stage liver disease with sodium (MELD-Na), and the CLIF Consortium Acute Decompensation (CLIF-C AD) models. DESIGN: We analysed a retrospective cohort of 73 976 patients comprising 247 650 hospitalisations between 2006 and 2013 at any of 123 Department of Veterans Affairs hospitals. Using 45 predictor variables, we built a time-dependent Cox proportional hazards model with all-cause mortality as the outcome. We compared performance to the three extant models and reported discrimination and calibration using bootstrapping. Furthermore, we analysed differential utility using the net reclassification index (NRI). RESULTS: The C-statistic for the final model was 0.863, representing a significant improvement over the MELD, MELD-Na, and the CLIF-C AD, which had C-statistics of 0.655, 0.675, and 0.679, respectively. Multiple risk factors were significant in our model, including variables reflecting disease severity and haemodynamic compromise. The NRI showed a 24% improvement in predicting survival of low-risk patients and a 30% improvement in predicting death of high-risk patients. CONCLUSION: We developed a more accurate mortality risk prediction score using variables automatically extractable from an EHR that may be used to risk stratify patients with cirrhosis for targeted postdischarge management.

Background and Objective: Most evidence about difficult‐to‐treat and severe asthma (DTTA) comes from clinical trials and registries. We aimed to identify people with DTTA from a large nationally representative asthma population and describe their characteristics and healthcare utilization compared with people whose asthma was not 'difficult‐to‐treat'. Methods: We conducted a cross‐sectional survey of Australians aged ≥18 years with current asthma from large web‐based survey panels. Enrolment was stratified by gender, age‐group and state/territory based on national population data for people with asthma. Difficult‐to‐treat or severe asthma was defined by poor symptom control, exacerbations and/or oral corticosteroid/biologic use despite medium/high‐dose inhaled therapy. Outcomes included exacerbations, healthcare utilization, multimorbidity, quality of life and coronavirus disease of 2019 (COVID‐19)‐related behaviour. Weighted data were analysed using SAS version 9.4. Results: The survey was conducted in February–March 2021. The weighted sample comprised 6048 adults with current asthma (average age 47.3 ± SD 18.1 years, 59.9% female), with 1313 (21.7%) satisfying ≥1 DTTA criteria. Of these, 50.4% had very poorly controlled symptoms (Asthma Control Test ≤15), 36.2% were current smokers, and 85.4% had ≥1 additional chronic condition, most commonly anxiety/depression. More than twice as many participants with DTTA versus non‐DTTA had ≥1 urgent general practitioner (GP) visit (61.4% vs. 27.5%, OR 4.8 [4.2–5.5, p < 0.0001]), or ≥1 emergency room visit (41.9% vs. 17.9%, OR 3.8 [3.3–4.4, p < 0.0001]) in the previous 12 months. Conclusion: Our findings emphasize the burden of uncontrolled symptoms, current smoking, multimorbidity and healthcare utilization in people with DTTA in the community, who may be under‐represented in registries or clinical trials. In this cross‐sectional study of a nationally‐representative population of 6048 adults with current asthma, 21.7% had difficult‐to‐treat asthma. Burden for patients and health system was seen in poor general health status, poor asthma symptom control, frequent urgent GP and emergency department (ED)/hospital visits and high prevalence of multimorbidity, particularly anxiety and/or depression. See relatededitorial [ABSTRACT FROM AUTHOR]

OBJECTIVE:Hepatorenal Syndrome (HRS) is a devastating form of acute kidney injury (AKI) in advanced liver disease patients with high morbidity and mortality, but phenotyping algorithms have not yet been developed using large electronic health record (EHR) databases. We evaluated and compared multiple phenotyping methods to achieve an accurate algorithm for HRS identification. MATERIALS AND METHODS:A national retrospective cohort of patients with cirrhosis and AKI admitted to 124 Veterans Affairs hospitals was assembled from electronic health record data collected from 2005 to 2013. AKI was defined by the Kidney Disease: Improving Global Outcomes criteria. Five hundred and four hospitalizations were selected for manual chart review and served as the gold standard. Electronic Health Record based predictors were identified using structured and free text clinical data, subjected through NLP from the clinical Text Analysis Knowledge Extraction System. We explored several dimension reduction techniques for the NLP data, including newer high-throughput phenotyping and word embedding methods, and ascertained their effectiveness in identifying the phenotype without structured predictor variables. With the combined structured and NLP variables, we analyzed five phenotyping algorithms: penalized logistic regression, naïve Bayes, support vector machines, random forest, and gradient boosting. Calibration and discrimination metrics were calculated using 100 bootstrap iterations. In the final model, we report odds ratios and 95% confidence intervals. RESULTS:The area under the receiver operating characteristic curve (AUC) for the different models ranged from 0.73 to 0.93; with penalized logistic regression having the best discriminatory performance. Calibration for logistic regression was modest, but gradient boosting and support vector machines were superior. NLP identified 6985 variables; a priori variable selection performed similarly to dimensionality reduction using high-throughput phenotyping and semantic similarity informed clustering (AUC of 0.81 - 0.82). CONCLUSION:This study demonstrated improved phenotyping of a challenging AKI etiology, HRS, over ICD-9 coding. We also compared performance among multiple approaches to EHR-derived phenotyping, and found similar results between methods. Lastly, we showed that automated NLP dimension reduction is viable for acute illness.

ObjectivesTo determine the effectiveness of a statewide telephone service in identifying low-income women at risk for hereditary breast and ovarian cancer and referring them to free genetic counseling.MethodsFrom June 2010 through August 2011, eligible callers to California's toll-free breast and cervical cancer telephone service were screened for their family histories of breast and ovarian cancer. High-risk women were identified and called for a baseline survey and randomization to an immediate offer of genetic counseling or a mailed brochure on how to obtain counseling. Clinic records were used to assess receipt of genetic counseling after 2 months.ResultsAmong 1212 eligible callers, 709 (58.5%) agreed to answer family history questions; 102 (14%) were at high risk (25% Hispanic, 46% White, 10% Black, 16% Asian, 3% of other racial/ethnic backgrounds). Of the high-risk women offered an immediate appointment, 39% received counseling during the intervention period, as compared with 4.5% of those receiving the brochure.ConclusionsA public health approach to the rare but serious risk of hereditary breast and ovarian cancer can be successful when integrated into the efforts of existing safety net organizations.

Simple Summary: Congenital malformations of the equine caudal cervical vertebrae (C6 and C7) were first reported in the 1800s. However, by the 21st century these findings were no longer incidental, especially with studies reporting breed percentages as high as 38%. Therefore, this study examined the association between C6 osseous specimens (n = 85) demonstrating the congenital malformation of either a partial unilateral or bilateral absent ventral process and the transposition on the ventral surface of sequential C7s (44/85). The aim was to determine if the absent morphology on C6 corresponded to the transposition on C7 and in some cases replication of a transverse foramen (33/44). Findings revealed the larger morphological absence on C6 generated a stronger dependency for transposition on C7 that included the replication of a transverse foramen. The transposition on C7 was characterized by either a complete or incomplete likeness of the C6 ventral process. Complete transpositions on C7 appeared as a normal C6 ventral process, while incomplete transpositions appeared in most as a cranial ventral tubercle (CrVT). When complete bilateral transpositions were demonstrated, the morphology of C7 resembled C6. These findings are relevant to the reading of radiographic images and potential clinical findings. In recent years, equine complex vertebral malformation (ECVM) has been of concern in the equine community, with studies identifying numerous associative morphological variations. Here, we examine the morphological association between C6 and C7 for dependency in ECVM cases, where the partially absent ventral process of C6 transposes on the ventral surface of C7. A C6 ventral process presents two tubercles, one cranial (CrVT) and one caudal (CVT). In this study, the C6 osseous specimens (n = 85) demonstrated a partial or completely absent CVT (aCVT) graded 1–4 that often extended cranially creating a partially absent cranial ventral tubercle (aCrVT) graded 1–3. In the 85 C6 osseous specimens examined, the corresponding C7s demonstrated either a complete or incomplete transposition of the ventral process from C6 in 44/85, with 30/44 replicating a transverse foramen. A strong statistical dependency existed between C6 grade 4 aCVTs and grades 1–3 aCrVTs and C7 transpositions with replicated transverse foramen. Sidedness was also demonstrated, where a left sided absent C6 associated with transposition on the left ventral surface of C7. This likewise applied to right sidedness and most bilateral cases. These findings might benefit practitioners when radiographing the extent of the ECVM configuration in patients presenting caudal cervical pain. [ABSTRACT FROM AUTHOR]

PurposeAlthough cigarette smoking remains the most common risk factor for heart disease among the young, few studies have explored the relationship of smoking with heart disease mortality risk among young people. This prospective study assesses the risk and burden of all heart disease (HD) and coronary heart disease (CHD) mortality associated with smoking among younger adults from a nationally representative sample of the United States.MethodNational Health Interview Survey respondents' data from 1997-2004 were linked to their death records through 2006. The analyses were restricted to individuals 18 to 44 years of age during follow up (n = 121,284). Cox proportional hazard ratios (HR) were estimated with adjustment for sample weights and design effects. Attributable fractions (AF) of smoking were calculated.ResultsAfter controlling for age, race, body mass index, history of hypertension and diabetes, and leisure time physical activity, current smoking related CHD mortality HR was 14.6 [95 % confidence interval or CI, 3.3-64.9] for females and 3.6 [95 % CI, 1.2-10.4] for males. The HR for all HD mortality was 3.1 [95 % CI, 1.3-7.6] for females and 2.4 [95 % CI, 1.2-4.7] for males. The AF of smoking for CHD deaths for female and male were 0.58 and 0.54 respectively. The AF of all HD mortality was 0.31 for male and 0.32 for female. The mean estimates of all HD deaths attributable to smoking during 1997-2006 among this age group were 52,214, of which 45,147 were CHD deaths.ConclusionEven after adjustment for multiple risk factors and without addressing passive smoking, our result showed a strong relationship between smoking and HD and CHD mortality among young adults that is likely causal.

Understanding how students make music in non-school settings can inform teaching practice in schools, making teaching more relevant to students' musical perspectives. This research study examined the musical processes of a three-member rock band, their roles within the group, and considered how they constructed musical meaning. The most salient findings that emerged from this study lie at the intersection of musical growth, musical enculturation, and musical meaning. Collaborative composing was facilitated by shared musical tastes and grounded in friendship and commitment to music making. Engagement and investment in the music prompted meaningful musical experiences for group members. Ownership, agency, relevance, and personal expression fuse at the core of the value they place on this musical and social experience. Implications for the instrumental music classroom are also shared. (Contains 7 notes.)

Introduction The pitfalls of label leakage, contamination of model input features with outcome information, are well established. Unfortunately, avoiding label leakage in clinical prediction models requires more nuance than the common advice of applying "no time machine rule." Framework We provide a framework for contemplating whether and when model features pose leakage concerns by considering the cadence, perspective, and applicability of predictions. To ground these concepts, we use real-world clinical models to highlight examples of appropriate and inappropriate label leakage in practice. Recommendations Finally, we provide recommendations to support clinical and technical stakeholders as they evaluate the leakage tradeoffs associated with model design, development, and implementation decisions. By providing common language and dimensions to consider when designing models, we hope the clinical prediction community will be better prepared to develop statistically valid and clinically useful machine learning models. [ABSTRACT FROM AUTHOR]