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Background: Current methods for developing clinical practice guidelines have several limitations: they are characterised by the "black box" operation—a process with defined inputs and outputs but an incomplete understanding of its internal workings; they have "the integration problem"—a lack of framework for explicitly integrating factors such as patient preferences and trade‐offs between benefits and harms; they generate one recommendation at a time that typically are not connected in a coherent analytical framework; and they apply to "average" patients, while clinicians and their patients seek advice tailored to individual circumstances. Methods: We propose augmenting the current guideline development method by converting evidence‐based pathways into fast‐and‐frugal decision trees (FFTs) and integrating them with generalised decision curve analysis to formulate clear, individualised management recommendations. Results: We illustrate the process by developing recommendations for the management of heparin‐induced thrombocytopenia (HIT). We converted evidence‐based pathways for HIT, developed by the American Society of Hematology, into an FFT. Here, we consider only thrombotic complications and major bleeding. We leveraged the predictive potential of FFTs to compare the effects of argatroban, bivalirudin, fondaparinux, and direct oral anticoagulants (DOACs) using generalised decision curve analysis. We found that DOACs were superior to other treatments if the FFT‐predicted probability of HIT exceeded 3%. Conclusions: The proposed analytical framework connects guidelines, pathways, FFTs, and decision analysis, offering risk‐tailored personalised recommendations and addressing current guideline development critiques. [ABSTRACT FROM AUTHOR]

The aim of this pre‐ and post‐interventional study was to analyze the cost‐effectiveness of the multicomponent fall prevention program in hospitalized patients. To achieve this aim, cost‐effectiveness analysis performed using decision tree modeling was compared with the implementation of the fall prevention program and usual care. The primary outcome was the number of patient falls. The uncertainty in cost and effectiveness data was evaluated using one‐way sensitivity analysis, best–worst‐case scenario analysis, and probabilistic sensitivity analysis. According to cost‐effectiveness analysis, implementation of the fall prevention program was dominantly cost‐effective. As a result of the probabilistic sensitivity analysis, it was revealed that, even if willing‐to‐pay per‐fall prevented value was 0, the probability of being cost‐effective was 54.4% for the fall prevention program. Economic evaluation results showed that implementing the multicomponent fall prevention program was dominantly cost‐effective in hospitalized patients. Nurses and nurse managers can benefit from economic evaluations in their decision‐making processes to implement fall prevention programs. [ABSTRACT FROM AUTHOR]

Objective: To determine the cost‐effectiveness of a virtual version of the Body Project (vBP), a cognitive dissonance‐based program, to prevent eating disorders (ED) among young women with a subjective sense of body dissatisfaction in the Swedish context. Method: A decision tree combined with a Markov model was developed to estimate the cost‐effectiveness of the vBP in a clinical trial population of 149 young women (mean age 17 years) with body image concerns. Treatment effect was modeled using data from a trial investigating the effects of vBP compared to expressive writing (EW) and a do‐nothing alternative. Population characteristics and intervention costs were sourced from the trial. Other parameters, including utilities, treatment costs for ED, and mortality were sourced from the literature. The model predicted the costs and quality‐adjusted life years (QALYs) related to the prevention of incidence of ED in the modeled population until they reached 25 years of age. The study used both a cost‐utility and return on investment (ROI) framework. Results: In total, vBP yielded lower costs and larger QALYs than the alternatives. The ROI analysis denoted a return of US $152 for every USD invested in vBP over 8 years against the do‐nothing alternative and US $105 against EW. Discussion: vBP is likely to be cost‐effective compared to both EW and a do‐nothing alternative. The ROI from vBP is substantial and could be attractive information for decision makers for implementation of this intervention for young females at risk of developing ED. Public Significance: This study estimates that the vBP is cost‐effective for the prevention of eating disorders among young women in the Swedish setting, and thus is a good investment of public resources. [ABSTRACT FROM AUTHOR]

Personalized medicine aims at identifying best treatments for a patient with given characteristics. It has been shown in the literature that these methods can lead to great improvements in medicine compared to traditional methods prescribing the same treatment to all patients. Subgroup identification is a branch of personalized medicine, which aims at finding subgroups of the patients with similar characteristics for which some of the investigated treatments have a better effect than the other treatments. A number of approaches based on decision trees have been proposed to identify such subgroups, but most of them focus on two-arm trials (control/treatment) while a few methods consider quantitative treatments (defined by the dose). However, no subgroup identification method exists that can predict the best treatments in a scenario with a categorical set of treatments. We propose a novel method for subgroup identification in categorical treatment scenarios. This method outputs a decision tree showing the probabilities of a given treatment being the best for a given group of patients as well as labels showing the possible best treatments. The method is implemented in an R package psica available on CRAN. In addition to a simulation study, we present an analysis of a community-based nutrition intervention trial that justifies the validity of our method. [ABSTRACT FROM AUTHOR]

Background: Nurses in neurointensive care units (NCUs) commonly use physical restraint (PR) to prevent adverse events like unplanned removal of devices (URDs) or falls. However, PR use should be based on evidenced decisions as it has drawbacks. Unfortunately, there is a lack of research‐based PR protocol to support decision‐making for nurses, especially for neurocritical patients. Aim: This study developed a restraint decision tree for neurocritical patients (RDT‐N) to assist nurses in making PR decisions. We assessed its effectiveness in reducing PR use and adverse events. Study Design: This study employed a baseline and post‐intervention test design at a NCU with 19 beds and 45 nurses in a tertiary hospital in a metropolitan city in South Korea. Two‐hundred and thirty‐seven adult patients were admitted during the study period. During the intervention, nurses were trained on the RDT‐N. PR use and adverse events between the baseline and post‐intervention periods were compared. Results: Post‐intervention, total number of restrained patients decreased (20.7%–16.3%; χ2 = 7.68, p =.006), and the average number of PR applied per restrained patient decreased (2.42–1.71; t = 5.74, p <.001). The most frequently used PR type changed from extremity cuff to mitten (χ2 = 397.62, p <.001). No falls occurred during the study periods. On the other hand, URDs at baseline were 18.67 cases per 1000 patient days in the high‐risk group and 5.78 cases per 1000 patient days in the moderate‐risk group; however, no URD cases were reported post‐intervention. Conclusions: The RDT‐N effectively reduced PR use and adverse events. Its application can enhance patient‐centred care based on individual condition and potential risks in NCUs. Relevance to Clinical Practice: Nurses can use the RDT‐N to assess the need for PR in caring for neurocritical patients, reducing PR use and adverse events. [ABSTRACT FROM AUTHOR]

Injury surveillance data indicate that collegiate athletes are at greater risk for lower extremity (LE) injuries following sports‐related concussion (SRC). While the association between SRC and LE injury appears to be clinically relevant up to 1‐year post‐SRC, little evidence has been provided to determine possible mechanistic rationales. Thus, we aimed to compare collegiate athletes with a history of SRC to matched controls on biomechanical and cognitive performance measures associated with LE injury risk. Athletes with a history of SRC (n = 20) and matched controls (n = 20) performed unanticipated bilateral land‐and‐cut tasks and cognitive assessments. Group‐based analyses (ANOVA) and predictive modeling (C5.0 decision tree algorithm) were used to compare group differences on biomechanical and cognitive measures. Collegiate athletes with a history of SRC demonstrated approximately six degrees less peak knee flexion on both dominant (p = 0.03, d = 0.71) and nondominant (p = 0.02, d = 0.78) limbs during the land‐and‐cut tasks compared to controls. Verbal Memory, knee flexion, and Go/No Go total score (C5.0 decision tree algorithm) were identified as the strongest indicators of previous SRC injury history. Reduced knee flexion during sport‐specific land‐and‐cut tasks may be a mechanism for increased LE injury risk in athletes with a history of SRC. There appears to be multiple biomechanical and cognitive predictors for identifying previous SRC in collegiate athletes, providing evidence to support a multifactorial SRC management strategy to reduce future injury risk. [ABSTRACT FROM AUTHOR]

Rationale, Aims and Objectives: The development of clinical practice guidelines (CPG) suffers from the lack of an explicit and transparent framework for synthesising the key elements necessary to formulate practice recommendations. We matched deliberations of the American Society of Haematology (ASH) CPG panel for the management of pulmonary embolism (PE) with the corresponding decision‐theoretical constructs to assess agreement of the panel recommendations with explicit decision modelling. Methods: Five constructs were identified of which three were used to reformulate the panel's recommendations: (1) standard, expected utility threshold (EUT) decision model; (2) acceptable regret threshold model (ARg) to determine the frequency of tolerable false negative (FN) or false positive (FP) recommendations, and (3) fast‐and‐frugal tree (FFT) decision trees to formulate the entire strategy for management of PE. We compared four management strategies: withhold testing versus d‐dimer → computerized pulmonary angiography (CTPA) ('ASH‐Low') versus CTPA→ d‐dimer ('ASH‐High') versus treat without testing. Results: Different models generated different recommendations. For example, according to EUT, testing should be withheld for prior probability PE < 0.13%, a clinically untenable threshold which is up to 15 times (2/0.13) below the ASH guidelines threshold of ruling out PE (at post probability of PE ≤ 2%). Three models only agreed that the 'ASH low' strategy should be used for the range of pretest probabilities of PE between 0.13% and 13.27% and that the 'ASH high' management should be employed in a narrow range of the prior PE probabilities between 90.85% and 93.07%. For all other prior probabilities of PE, choosing one model did not ensure coherence with other models. Conclusions: CPG panels rely on various decision‐theoretical strategies to develop its recommendations. Decomposing CPG panels' deliberation can provide insights if the panels' deliberation retains a necessary coherence in developing guidelines. CPG recommendations often do not agree with the EUT decision analysis, widely used in medical decision‐making modelling. [ABSTRACT FROM AUTHOR]

Objective: Body mass index (BMI) is the primary criterion differentiating anorexia nervosa (AN) and atypical anorexia nervosa despite prior literature indicating few differences between disorders. Machine learning (ML) classification provides us an efficient means of accurately distinguishing between two meaningful classes given any number of features. The aim of the present study was to determine if ML algorithms can accurately distinguish AN and atypical AN given an ensemble of features excluding BMI, and if not, if the inclusion of BMI enables ML to accurately classify between the two. Methods: Using an aggregate sample from seven studies consisting of individuals with AN and atypical AN who completed baseline questionnaires (N = 448), we used logistic regression, decision tree, and random forest ML classification models each trained on two datasets, one containing demographic, eating disorder, and comorbid features without BMI, and one retaining all features and BMI. Results: Model performance for all algorithms trained with BMI as a feature was deemed acceptable (mean accuracy = 74.98%, mean area under the receiving operating characteristics curve [AUC] = 74.75%), whereas model performance diminished without BMI (mean accuracy = 59.37%, mean AUC = 59.98%). Discussion: Model performance was acceptable, but not strong, if BMI was included as a feature; no other features meaningfully improved classification. When BMI was excluded, ML algorithms performed poorly at classifying cases of AN and atypical AN when considering other demographic and clinical characteristics. Results suggest a reconceptualization of atypical AN should be considered. Public Significance: There is a growing debate about the differences between anorexia nervosa and atypical anorexia nervosa as their diagnostic differentiation relies on BMI despite being similar otherwise. We aimed to see if machine learning could distinguish between the two disorders and found accurate classification only if BMI was used as a feature. This finding calls into question the need to differentiate between the two disorders. [ABSTRACT FROM AUTHOR]

Background: Before COVID‐19, people with intellectual disabilities were digitally excluded, facing difficulties accessing and using technology. The pandemic outbreak closed down many in‐person services and supports for this population. Methods: This study examined changes in technology use to support the healthcare and social connections of older adults with intellectual disability in Ireland (n = 682) during COVID‐19. Decision tree analyses explored factors associated with using technology for health and social connection; and to explore relationships between technology use and subjective outcomes of stress/anxiety and of positives during this period. Findings: Technology use for healthcare and social connection increased during COVID‐19, but not enough to make up for the loss of in‐person care/connection. Residence type was associated with increased use of technology for healthcare utilisation, with the highest rates among community group home residents and the lowest among those in independent/family settings. Increased technology use to speak with both family and friends was associated with prior digital skills and digital access. Increased technology use was significantly associated with both positive and negative experiences during the pandemic. Conclusions: The COVID‐19 pandemic offered an opportunity for increased digital connection for older adults with intellectual disabilities, but not all benefitted. Those with prior skills, available supports, and the greatest need to connect were more likely to enhance their use of technology. The complexity of the COVID‐19 period, including the general impact on mental health and well‐being, means that the role technology played in supporting quality of life and mitigating stress/anxiety needs further and longer‐term investigation. Accessible summary: Older adults with an intellectual disability in Ireland used technology more during the COVID‐19 pandemic—to use health services, and to speak to family and friends.Having digital skills before COVID‐19 helped people to increase their use of technology.Technology helped many people to stay connected, but did not fully make up for the loss of in‐person services and contact.Participants who used technology more during COVID‐19 were more likely to report some positive things during COVID‐19.Participants who used technology more were also more likely to feel stress or anxiety during COVID‐19.This shows that COVID‐19 was difficult for many people. But the study also shows that most people also found some good things during this time. [ABSTRACT FROM AUTHOR]

Coauthorship prediction applies predictive analytics to bibliographic data to predict authors who are highly likely to be coauthors. In this study, we propose an approach for coauthorship prediction based on bibliographic network embedding through a graph‐based bibliographic data model that can be used to model common bibliographic data, including papers, terms, sources, authors, departments, research interests, universities, and countries. A real‐world dataset released by AMiner that includes more than 2 million papers, 8 million citations, and 1.7 million authors were integrated into a large bibliographic network using the proposed bibliographic data model. Translation‐based methods were applied to the entities and relationships to generate their low‐dimensional embeddings while preserving their connectivity information in the original bibliographic network. We applied machine learning algorithms to embeddings that represent the coauthorship relationships of the two authors and achieved high prediction results. The reference model, which is the combination of a network embedding size of 100, the most basic translation‐based method, and a gradient boosting method achieved an F1 score of 0.9 and even higher scores are obtainable with different embedding sizes and more advanced embedding methods. Thus, the strengths of the proposed approach lie in its customizable components under a unified framework. [ABSTRACT FROM AUTHOR]

Rationale, Aims and Objectives: Critics have charged that evidence‐based medicine (EBM) overemphasises algorithmic rules over unstructured clinical experience and intuition, but the role of structured decision support systems in improving health outcomes remains uncertain. We aim to assess if delivery of anticoagulant prophylaxis in hospitalised patients with COVID‐19 according to an algorithm based on evidence‐based clinical practice guideline (CPG) improved clinical outcomes compared with administration of anticoagulant treatment given at individual practitioners' discretion. Methods: An observational design consisting of the analysis of all acutely ill, consecutive patients (n = 1783) with confirmed COVID‐19 diagnosis admitted between 10 March 2020 to 11 January 2022 to an US academic center. American Society of Haematology CPG for anticoagulant prophylaxis in hospitalised patients with COVID‐19 was converted into a clinical pathway and translated into fast‐and‐frugal decision (FFT) tree ('algorithm'). We compared delivery of anticoagulant prophylaxis in hospitalised patients with COVID‐19 according to the FFT algorithm with administration of anticoagulant treatment given at individual practitioners' discretion. Results: In an adjusted analysis, using combination of Lasso (least absolute shrinkage and selection operator) and propensity score based weighting [augmented inverse‐probability weighting] statistical techniques controlling for cluster data, the algorithm did not reduce death, venous thromboembolism, or major bleeding, but helped avoid longer hospital stay [number of patients needed to be treated (NNT) = 40 (95% CI: 23–143), indicating that for every 40 patients (23–143) managed on FFT algorithm, one avoided staying in hospital longer than 10 days] and averted admission to intensive‐care unit (ICU) [NNT = 19 (95% CI: 13–40)]. All model's selected covariates were well balanced. The results remained robust to sensitivity analyses used to test the stability of the findings. Conclusions: When delivered using a structured FFT algorithm, CPG shortened the hospital stay and help avoided admission to ICU, but it did not affect other relevant outcomes. [ABSTRACT FROM AUTHOR]

Background: Several studies have proposed grading systems for risk stratification of early‐stage lung adenocarcinoma based on histological patterns. However, the reproducibility of these systems is poor in clinical practice, indicating the need to develop a new grading system which is easy to apply and has high accuracy in prognostic stratification of patients. Methods: Patients with stage I invasive nonmucinous lung adenocarcinoma were retrospectively collected from pathology archives between 2009 and 2016. The patients were divided into a training and validation set at a 6:4 ratio. Histological features associated with patient outcomes (overall survival [OS] and progression‐free survival [PFS]) identified in the training set were used to construct a new grading system. The newly proposed system was validated using the validation set. Survival differences between subgroups were assessed using the log‐rank test. The prognostic performance of the novel grading system was compared with two previously proposed systems using the concordance index. Results: A total of 539 patients were included in this study. Using a multioutcome decision tree model, four pathological factors, including the presence of tumor spread through air space (STAS) and the percentage of lepidic, micropapillary and solid subtype components, were selected for the proposed grading system. Patients were accordingly classified into three groups: low, medium, and high risk. The high‐risk group showed a 5‐year OS of 52.4% compared to 89.9% and 97.5% in the medium and low‐risk groups, respectively. The 5‐year PFS of patients in the high‐risk group was 38.1% compared to 61.7% and 90.9% in the medium and low‐risk groups, respectively. Similar results were observed in the subgroup analysis. Additionally, our proposed grading system provided superior prognostic stratification compared to the other two systems with a higher concordance index. Conclusion: The newly proposed grading system based on four pathological factors (presence of STAS, and percentage of lepidic, micropapillary, and solid subtypes) exhibits high accuracy and good reproducibility in the prognostic stratification of stage I lung adenocarcinoma patients. [ABSTRACT FROM AUTHOR]

Background: To realize the potential of precision medicine, predictive models should be integrated within the framework of decision analysis, such as the decision curve analysis (DCA). To date, its application has required individual patient data (IPD) that are often unavailable. Performing DCA using aggregate data without requiring IPD may advance the goals of precision medicine. Methods: We present a statistical framework demonstrating that DCA can be conducted by using only the mean and standard deviation (SD) from the raw probabilities of the predictive model. We tested our theoretical framework by performing extensive simulations and comparing the aggregate‐based DCA with IPD DCA. The latter was conducted using IPD from four predictive models that employed logistic regression, Cox or competing risk time‐to‐event modeling including (a) statins for primary prevention of cardiovascular disease (n = 4859), (b) hospice referral for terminally ill patients (n = 9104), (c) use of thromboprophylaxis for preventing venous thromboembolism in patients with cancer (n = 425) and (d) prevention of sinusoidal obstruction syndrome after hematopoietic cell transplantation (SCT) (n = 80). Results: Simulations assuming perfect calibration showed that regardless of which probability distributions informed the predictive models, the differences in DCA were negligible. Similarly, for the adequately powered models, the results of DCA based on the summary data were similar to IPD‐derived DCA. The inherent instability of the predictive models, based on the smaller sample sizes, resulted in a somewhat larger discrepancy between aggregate and IPD‐based DCA. Conclusions: DCA informed by adequately powered and well‐calibrated models using only summary statistical estimates (mean and SD) approximates well models using IPD. Use of aggregate data will facilitate broader integration of predictive with decision modeling toward the goals of individualized decision‐making. [ABSTRACT FROM AUTHOR]

Background: This study aimed to investigate the value of nonenhanced computed tomography (CT)‐based radiomics in determining disease progression in breast cancer patients with bone marrow metastases and to develop a model for assessing treatment efficacy. Methods: A total of 134 breast cancer patients with bone metastases were enrolled from three hospitals. Nonenhanced CT was performed after two cycles of drug treatment. The images were categorized into an invalid and a valid group according to disease progression status. The largest osteolytic lesions' maximum cross‐sections in the CT images were selected as regions of interest (ROIs) for feature extraction. Variance threshold, SelectKBest, and least absolute shrinkage and selection operator (LASSO) were used to reduce feature dimensionality. K‐nearest neighbor algorithm (KNN), support vector machine (SVM), extreme gradient boosting (XGBoost), random forest (RF), logistic regression (LR), and decision tree (DT) algorithms were trained to establish radiomics models. Receiver operating characteristic (ROC) curves were generated to evaluate the diagnostic performance of the models. Results: The KNN classifier demonstrated the best performance compared to the random grouping method. In the validation group, the area under the ROC curve (AUC) was 0.810. In the cross‐validation method, the RF classifier showed the best performance with an AUC of 0.84. Conclusion: Nonenhanced CT‐based radiomics provides a promising method for evaluating the efficacy of systemic drug therapy in breast cancer patients with osteolytic bone metastases. [ABSTRACT FROM AUTHOR]

Improving health literacy through health information dissemination is one of the most economical and effective mechanisms for improving population health. This process needs to fully accommodate the thematic suitability of health information supply and demand and reduce the impact of information overload and supply–demand mismatch on the enthusiasm of health information acquisition. We propose a health information topic modeling analysis framework that integrates deep learning methods and clustering techniques to model the supply‐side and demand‐side topics of health information and to quantify the thematic alignment of supply and demand. To validate the effectiveness of the framework, we have conducted an empirical analysis on a dataset with 90,418 pieces of textual data from two prominent social networking platforms. The results show that the supply of health information in general has not yet met the demand, the demand for health information has not yet been met to a considerable extent, especially for disease‐related topics, and there is clear inconsistency between the supply and demand sides for the same health topics. Public health policy‐making departments and content producers can adjust their information selection and dissemination strategies according to the distribution of identified health topics, thereby improving the effectiveness of public health information dissemination. [ABSTRACT FROM AUTHOR]

Diabetic foot ulcers affect quality of life and economically burden patients and the Indonesian healthcare system. The comparative cost‐effectiveness of wound care specialists in private practices (e.g., wound clinics) and wound care nurses in national hospitals remains unknown. Thus, we used a decision tree to compare the cost and healing rates for patients after 12 weeks of wound care. Uncertainty was addressed using one‐way and probabilistic sensitivity analyses. Among 89 participants (42 in the national hospital and 47 in the private practice), no significant differences were observed between the two groups in terms of sex, age, education level, smoking status, duration of diabetes, Wagner wound classification, glycated haemoglobin levels, neuropathy status, ankle‐brachial index, baseline characteristics, quality of life, DMIST (depth, maceration, inflammation/infection, size, tissue type of the wound bed, type of wound edge, and tunnelling/undermining) score and wound location (p > 0.05). However, significant differences were observed for days from first visit/assessment until complete healing, mean quality of life (p ≤ 0.001) and wound size (p = 0.047). Wound care specialists in private practices had a significantly lower cost of 2,804,423.3 Indonesian rupiah compared to 6,483,493.4 Indonesian rupiah for wound care nurses in national hospitals. The incremental cost‐effectiveness ratio was −165,723.9. Therefore, wound care specialists in private practices are more cost‐effective for managing diabetic foot ulcers. Probability sensitivity analysis confirmed that 80%–90% of the scenarios were cost‐effective. These findings may inform healthcare resource allocation in Indonesia. Additionally, evidence‐based cost‐effectiveness measures were strengthened in private practices and national hospitals. [ABSTRACT FROM AUTHOR]

Diabetic foot ulcers (DFU) are one of the most frequent and debilitating complications of diabetes. DFU wound healing is a highly complex process, resulting in significant medical, economic and social challenges. Therefore, early identification of patients with a high‐risk profile would be important to adequate treatment and more successful health outcomes. This study explores risk assessment profiles for DFU healing and healing prognosis, using machine learning predictive approaches and decision tree algorithms. Patients were evaluated at baseline (T0; N = 158) and 2 months later (T1; N = 108) on sociodemographic, clinical, biochemical and psychological variables. The performance evaluation of the models comprised F1‐score, accuracy, precision and recall. Only profiles with F1‐score >0.7 were selected for analysis. According to the two profiles generated for DFU healing, the most important predictive factors were illness representations on T1 IPQ‐B (IPQ‐B ≤ 9.5 and < 10.5) and the DFU duration (≤ 13 weeks). The two predictive models for DFU healing prognosis suggest that biochemical factors are the best predictors of a favorable healing prognosis, namely IL‐6, microRNA‐146a‐5p and PECAM‐1 at T0 and angiopoietin‐2 at T1. Illness perception at T0 (IPQ‐B ≤ 39.5) also emerged as a relevant predictor for healing prognosis. The results emphasize the importance of DFU duration, illness perception and biochemical markers as predictors of healing in chronic DFUs. Future research is needed to confirm and test the obtained predictive models. [ABSTRACT FROM AUTHOR]

Problem: Autism spectrum disorders (ASD) are pervasive neurodevelopmental disorders and generally accompanied by social disorders, verbal or nonverbal communication defects, inability to concentrate and other negative symptoms that affect the autistic person's normal life. However, traditional screening methods are time‐consuming and public health resources are limited. Methods: This study proposed a novel technique that combined eye‐movement data and machine learning algorithms for predicting autistic traits. We converted raw eye movement data into features, trained and tested a model for early screening. Findings: In the preliminary experiment, 107 participants (average age = 24.84 ± 5.24 years) wore HTC Vive to watch a VR scene for 15–20 s. We explored eight classification models, among which the ensemble model performed best, with 0.73 accuracy, 0.68 precision, 0.81 recall, 0.74 F1‐score, and an area under the curve of 0.90. And in the test experiment, 22 participants (average age = 12.68 ± 7.61 years) diagnosed as ASD took the experiment and the ensemble model showed a recall of 0.77. Eye movement data is an effectively distinguishable tool and we find that the proportion of time to observe figure and animal region continuously can distinguish participants with obvious and unobvious autistic traits effectively in the model. Conclusion: This study focuses on the detection of autistic traits, and proposes a more objective and faster method for undertaking early screening, which provides possibilities to save precious time to intervene and alleviate its symptoms before making a definite diagnosis. [ABSTRACT FROM AUTHOR]

Objective: There is substantial heterogeneity among the phenotypes of patients with anti–melanoma differentiation–associated gene 5 antibody–positive (anti‐MDA5+) dermatomyositis (DM), hindering disease assessment and management. This study aimed to identify distinct phenotype groups in patients with anti‐MDA5+ DM and to determine the utility of these phenotypes in predicting patient outcomes. Methods: A total of 265 patients with anti‐MDA5+ DM were retrospectively enrolled in the study. An unsupervised hierarchical cluster analysis was performed to characterize the different phenotypes. Results: Patients were stratified into 3 clusters characterized by markedly different features and outcomes. Cluster 1 (n = 108 patients) was characterized by mild risk of rapidly progressive interstitial lung disease (RPILD), with the cumulative incidence of non‐RPILD being 85.2%. Cluster 2 (n = 72 patients) was characterized by moderate risk of RPILD, with the cumulative incidence of non‐RPILPD being 73.6%. Patients in cluster 3 (n = 85 patients), which was characterized by a high risk of RPILD and a cumulative non‐RPILD incidence of 32.9%, were more likely than patients in the other 2 subgroups to have anti–Ro 52 antibodies in conjunction with high titers of anti‐MDA5 antibodies. All‐cause mortality rates of 60%, 9.7%, and 3.7% were determined for clusters 3, 2, and 1, respectively (P < 0.0001). Decision tree analysis led to the development of a simple algorithm for anti‐MDA5+ DM patient classification that included the following 8 variables: age >50 years, disease course of <3 months, myasthenia (proximal muscle weakness), arthritis, C‐reactive protein level, creatine kinase level, anti–Ro 52 antibody titer, and anti‐MDA5 antibody titer. This algorithm placed patients in the appropriate cluster with 78.5% accuracy in the development cohort and 70.0% accuracy in the external validation cohort. Conclusion: Cluster analysis identified 3 distinct clinical patterns and outcomes in our large cohort of anti‐MDA5+ DM patients. Classification of DM patients into phenotype subgroups with prognostic values may help physicians improve the efficacy of clinical decision‐making. [ABSTRACT FROM AUTHOR]

Skin tissue assessment is traditionally used to identify early signs of pressure damage from changes observed at the skin surface. However, the early onset of tissue damage induced by pressure and shear forces is likely to be on soft tissues beneath the surface of the skin. Subepidermal moisture (SEM) is a biophysical marker for the detection of early and deep pressure‐induced tissue damage. Measurement of SEM can detect early pressure ulcers up to 5 days before visible skin changes occur. The aim of this study was to evaluate the cost‐effectiveness of SEM measurement compared with visual skin assessment (VSA). A decision‐tree model was developed. Outcomes are the incidence of hospital‐acquired pressure ulcers, quality‐adjusted life‐years (QALYs) and costs to the UK National Health Service. Costs are at 2020/21 prices. The effects of parameter uncertainty are tested in univariate and probabilistic sensitivity analysis. In a representative NHS acute hospital, the incremental cost of SEM assessment as an adjunct to VSA is −£8.99 per admission, and SEM assessment is expected to reduce the incidence of hospital‐acquired pressure ulcers by 21.1%, reduce NHS costs and lead to a gain of 3.634 QALYs. The probability of cost‐effectiveness at a threshold of £30 000 per quality‐adjusted life year is 61.84%. Pathways that include SEM assessment make it possible to implement early and anatomy‐specific interventions which have the potential to improve the effectiveness of pressure ulcer prevention and reduce healthcare costs. [ABSTRACT FROM AUTHOR]

Aims: Medication‐taking behaviours of breast cancer survivors undergoing adjuvant hormone therapy have received considerable attention. This study aimed to determine factors affecting medication‐taking behaviours in people with breast cancer using data mining. Design: A longitudinal observational retrospective cohort study with a hospital‐based survey. Methods: A total of 385 subjects were surveyed, analysing existing data from January 2010 to December 2017 in Taiwan. Three data mining approaches—multiple logistic regression, decision tree and artificial neural network—were used to build the prediction models and rank the importance of influencing factors. Accuracy, specificity and sensitivity were used as assessment indicators for the prediction models. Results: Multiple logistic regression was the most effective approach, achieving an accuracy of 96.37%, specificity of 96.75% and sensitivity of 96.12%. The duration of adjuvant hormone therapy discontinuation, duration of adjuvant hormone therapy use and age at diagnosis by data mining were the three most critical factors influencing the medication‐taking behaviours of people with breast cancer. [ABSTRACT FROM AUTHOR]

It is now common for individuals to have multiple computing devices, such as laptops, smart phones, and tablets. This multidevice environment increases the popularity of cross‐device search activities. Cross‐device search can be seen as a special case of cross‐session search. Previous studies regarded re‐finding behaviors in cross‐session search as task resumption. Based on this, this article proposes considering 2 phases of cross‐device search: task preparation and task resumption and to explore their features by modeling. A within‐subject user experiment was designed to collect data. Four groups of features were captured from specific behaviors of querying, clicking, gazing, and cognition. This article tested 3 machine‐learning methods and found that the C5.0 decision tree performed best. Five features were included in the task preparation behavior model, and 3 in the task resumption behavior model. The difference and relationship between task preparation and task resumption were investigated by comparing their behavioral features. It is concluded that information need remains blurred in task preparation and becomes clear in task resumption. The changing states of information need suggest an exploratory process in cross‐device search. We also identify some implications for search engine designers. [ABSTRACT FROM AUTHOR]

Background and objective: Variations in the risk factors for sarcopenia can lead to differences in the likelihood of developing sarcopenia among older adults; however, few studies have explored the interactions among the risk factors. This study examined the interactions among risk factors and identified a discriminative pathway for groups at risk of sarcopenia in community‐dwelling older adults. Methods: A cross‐sectional study was conducted between July and August 2019 to recruit 200 older adults from an outpatient department of a hospital providing care for older people. Data on various risk factors, namely demographics (age, gender, education, comorbidities, and body mass index [BMI]), dietary habits (weekly consumption of milk, coffee, and meat), lifestyle behaviours (vitamin D supplementation, smoking, drinking, and physical activity), and depression symptoms were collected. Sarcopenia was defined according to the Asian Working Group for Sarcopenia criteria. A classification and regression tree (CART) model was used to examine interactions among these factors and identify groups at risk of sarcopenia. Findings: The prevalence of sarcopenia was 38.5%. The CART model identified two end groups at differential risks of sarcopenia, with a minimum of one and a maximum of three risk factors. In the first group, low BMI (<18.5 kg/m2) was a predominant risk factor for sarcopenia among older people. In the second group, older adults with a normal BMI, aged ≥68 years, and without a regular walking habit had a higher probability of developing sarcopenia than did their counterparts. Conclusions: The interactive effects among older age, BMI, and walking may cause different probabilities of developing sarcopenia in the older population. Implications for practice: Older adults with a low or normal BMI but without a regular walking habit could be a predominant risk group for sarcopenia. The appropriate maintenance of body weight and regular walking activity is suggested to prevent sarcopenia in community‐dwelling older adults. [ABSTRACT FROM AUTHOR]

Like several indigenous populations, Sauria Paharias, a vulnerable indigenous tribal group residing in a biodiverse environment of Jharkhand, India, have high levels of undernutrition. We assessed agroforestry and dietary diversity, food consumption especially indigenous food (IF) intake and nutritional status of Sauria Paharia women through a cross‐sectional study conducted in 18 villages of Godda district, Jharkhand. Household level information was elicited through household surveys including a dietary survey and a food frequency questionnaire. Twenty‐four‐hour dietary recalls (24 HDR) and anthropometric assessments were taken on one randomly selected woman per household. An index, Food Accessed Diversity Index (FADI) created to measure agroforestry diversity, showed a low mean score of 0.21 ± 0.15 and range: 0, 0.85. Fifty‐nine percent of women consumed any IF during 24 HDR. Median minimum dietary diversity score for women (MDD‐W) was 3 (acceptable score ≥5). More than 96% of women had intakes below estimated average requirements for all nutrients studied (energy; vitamins A, C, thiamine, riboflavin, niacin, pyridoxine; folate; iron; calcium and zinc) except protein; 41% women were underweight. IF consumption was independently associated with calcium and vitamin A intake. Decision trees developed for micronutrient consumption at different levels of MDD‐W score and IF consumption scenarios revealed 1.3 to 2.9 times higher consumption of micronutrients among women with MDD‐W ≥ 3 or 4. Strategies like agricultural extension programmes promoting indigenous varieties and nutrition education for increasing dietary diversity with IFs have potential to address undernutrition in Sauria Paharia women. [ABSTRACT FROM AUTHOR]

Introduction: The occupational therapy profession has an essential role to play in healthy ageing that includes enabling participation, a construct that according to The International Classification of Functioning, Disability and Health (ICF), incorporates an environmental context. Environmental barriers and enablers of participation in community‐situated activities for people over the age of 65 have been identified. To support practice, occupational therapists require assessments with demonstrated content validity including comprehensive coverage of the construct. The purpose of this scoping review study was to investigate what instruments are available to assess community participation for people over the age of 65 that included environmental factors. Methods: A scoping review of the literature was conducted, utilising the Joanna Briggs Institute (JBI) scoping review methodology. The evidence source was review articles and inclusion criteria were that they reviewed instruments to assess participation that could be used for people over the age of 65. Items extracted from included instruments were evaluated against a preset list of community‐participation and environment categories that had been developed from the ICF. Results: Twenty‐three review studies met inclusion criteria and from these 240 instruments were extracted. Twenty instruments were retained after exclusions and from these, 540 instrument items were extracted. Of these, 280 (47%) were coded as community‐participation, and only 20 (3.4%) as environment items. Fourteen of the instruments included no environment items. Conclusions: No instrument was identified that comprehensively assessed community participation including the related environmental factors. Such an instrument is required to enable occupational therapy practitioners to support healthy ageing. The development of such an instrument will strengthen the profession's capacity to develop new ways of delivering services to older adults in line with emerging ways that aged care will be delivered and to advance its essential role in healthy ageing. [ABSTRACT FROM AUTHOR]

Aim: Medical nutrition therapy is a cornerstone treatment in gestational diabetes; however, most Australian women diagnosed with gestational diabetes do not receive this. The project evaluated adaptation of a successful evidence‐based gestational diabetes model of care implementation from a tertiary centre into regional sites with varied demographics, population size and service capacity. Methods: The project used a hub (project team)‐spoke (sites) model in Far North Queensland (Site 1) and regional South‐East Queensland (Site 2). Sites selected demonstrated strong gestational diabetes team cohesiveness and project commitment. The project phases were consultation, baseline, transition and implementation. A best practice decision tree tool was provided to assess/manage barriers to the model of care and clinical outcomes captured through a project database. Results: Role clarification of site members, management engagement, site visits, decision tree and database refinement were completed in the project's first phase. Unexpected organisational and team barriers prevented timeline implementation as planned. Sites negotiated relevant reallocation of resources to achieve project deliverables. The proportion of women seen according to best practice increased from 3.5 to 87.8% (P < 0.001) (Site 1) and nil to 4.8% (P = 0.09) (Site 2), and those on medication dropped by 3.4 (Site 1) and 9.1% (Site 2). Conclusions: This project demonstrates a successful implementation using a facilitated and rigorous approach. Support, engagement and tools at many levels were keys to success at both sites. The present study illustrates the opportunities and challenges of conducting implementation research within routine clinical care, particularly in resource‐challenged sites. [ABSTRACT FROM AUTHOR]

Objective: Evidence supports the validity of total knee arthroplasty (TKA) appropriateness classification based on the RAND Corporation and University of California Los Angeles system.The system, however, is ~2 decades old, and the science and clinical application of TKA has changed dramatically. We undertook this study to describe the methods used to develop a second-generation system and to examine the structure of the system to determine the extent to which each of the indication criteria informed appropriateness.Methods: Multivariable multinomial regression analyses determined the extent to which each of the 8 individually analyzed indication criteria informed appropriateness judgments. Classification tree analysis illustrates how the indication criteria, in combination, led to judgments of appropriate, inappropriate, or uncertain.Results: An expert panel selected 8 indication criteria (i.e., age, knee pain, function, radiographic osteoarthritis severity, osteoarthritis location, psychological factors, pain catastrophizing, and comorbidities). A total of 1,008 clinical scenarios were written, based on the criteria. Regression analyses indicated that age, knee pain, function, and radiographic severity dominated prediction of appropriateness, while the other criteria played a smaller role. Classification tree analysis confirmed the regression findings.Conclusion: Our second-generation classification system, which incorporates contemporary indicators of TKA prognosis and risk, demonstrated preliminary evidence for utility in clinical practice. [ABSTRACT FROM AUTHOR]

The coronavirus pandemic has exposed healthcare professionals to suffering and stressful working conditions. The aim of this study was to analyze professional quality of life among healthcare professionals and its relationship with empathy, resilience, and self-compassion during the COVID-19 crisis in Spain. A crosssectional study was conducted with 506 healthcare professionals, who participated by completing an online questionnaire. A descriptive correlational analysis was performed. A multivariate regression analysis and a decision tree were used to identify the variables associated with professional quality of life. Empathy, resilience, and mindfulness were the main predictors of compassion fatigue, compassion satisfaction, and burnout, respectively. [ABSTRACT FROM AUTHOR]

Aims: Pedal sensory loss due to diabetes-related neuropathy can be graded by testing static two-point discrimination (S2PD), moving two-point discrimination (M2PD), static one-point discrimination (S1PD; eg, 10-g monofilament), and vibration sense and is included in the Rotterdam Diabetic Foot (RDF) Study Test Battery. The aim of this study is to investigate if decision tree modelling is able to reduce the number of tests needed in estimating pedal sensation.Methods: The 39-item RDF Study Test Battery (RDF-39) scores were collected from the prospective RDF study and included baseline (n = 416), first follow-up (n = 364), and second follow-up (n = 135) measurements, supplemented with cross-sectional control data from a previous study (n = 196). Decision tree analysis was used to predict total RDF-39 scores using individual test item data. The tree was developed using baseline RDF study data and validated in follow-up and control data. Spearman correlation coefficients assessed the reliability between the decision tree and original RDF-39.Results: The tree reduced the number of items from 39 to 3 in estimating the RDF-39 sum score. M2PD (hallux), S2PD (first dorsal web, fifth toe), vibration sense (interphalangeal joint), and S1PD (first dorsal web, fifth toe) measurements proved to be predictive. The correlation coefficients to original scores were high (0.76 to 0.91).Conclusions: The decision tree was successful at reducing the number of RDF Test Battery items to only 3, with high correlation coefficients to the scores of the full test battery. The findings of this study aids medical decision making by time efficiently estimating pedal sensory status with fewer tests needed. [ABSTRACT FROM AUTHOR]

Objective: The M6 risk-prediction model was published as part of a two-step protocol using an initial progesterone level of ≤ 2 nmol/L to identify probable failing pregnancies (Step 1) followed by the M6 model (Step 2). The M6 model has been shown to have good triage performance for stratifying women with a pregnancy of unknown location (PUL) as being at low or high risk of harboring an ectopic pregnancy (EP). This study validated the triage performance of the two-step protocol in clinical practice by evaluating the number of protocol-related adverse events and how effectively patients were triaged.Methods: This was a prospective multicenter interventional study of 3272 women with a PUL, carried out between January 2015 and January 2017 in four district general hospitals and four university teaching hospitals in the UK. The final pregnancy outcome was defined as: a failed PUL (FPUL), an intrauterine pregnancy (IUP) or an EP (including persistent PUL (PPUL)). FPUL and IUP were grouped as low-risk and EP/PPUL as high-risk PUL. Serum progesterone and human chorionic gonadotropin (hCG) levels were measured at presentation in all patients. If the initial progesterone level was ≤ 2 nmol/L, patients were discharged and were asked to have a follow-up urine pregnancy test in 2 weeks to confirm a negative result. If the progesterone level was > 2 nmol/L or a measurement had not been taken, hCG level was measured again at 48 h and results were entered into the M6 model. Patients were managed according to the outcome predicted by the protocol. Those classified as 'low risk, probable FPUL' were advised to perform a urine pregnancy test in 2 weeks and those classified as 'low risk, probable IUP' were invited for a scan a week later. When a woman with a PUL was classified as high risk (i.e. risk of EP ≥ 5%) she was reviewed clinically within 48 h. One center used a progesterone cut-off of ≤ 10 nmol/L and its data were analyzed separately. If the recommended management protocol was not adhered to, this was recorded as a protocol deviation and classified as: unscheduled visit for clinician reason, unscheduled visit for patient reason or incorrect timing of blood test or ultrasound scan. The classifications outlined in the UK Good Clinical Practice (GCP) guidelines were used to evaluate the incidence of adverse events. Data were analyzed using descriptive statistics.Results: Of the 3272 women with a PUL, 2625 were included in the final analysis (317 met the exclusion criteria or were lost to follow-up, while 330 were evaluated using a progesterone cut-off of ≤ 10 nmol/L). Initial progesterone results were available for 2392 (91.1%) patients. In Step 1, 407 (15.5%) patients were classified as low risk (progesterone ≤ 2 nmol/L), of whom seven (1.7%) were ultimately diagnosed with an EP. In 279 of the remaining 2218 women with a PUL, the M6 model was not applied owing to protocol deviation or because the outcome was already known (usually on the basis of an ultrasound scan) before a second hCG reading was taken; of these patients, 30 were diagnosed with an EP. In Step 2, 1038 women with a PUL were classified as low risk, of whom eight (0.8%) had a final outcome of EP. Of 901 women classified as high risk at Step 2, 275 (30.5%) had an EP. Therefore, 275/320 (85.9%) EPs were correctly classified as high risk. Overall, 1445/2625 PUL (55.0%) were classified as low risk, of which 15 (1.0%) were EP. None of these cases resulted in a ruptured EP or significant clinical harm. Sixty-two women participating in the study had an adverse event, but no woman had a serious adverse event as defined in the UK GCP guidelines.Conclusions: This study has shown that the two-step protocol incorporating the M6 model effectively triaged the majority of women with a PUL as being at low risk of an EP, minimizing the follow-up required for these patients after just two visits. There were few misclassified EPs and none of these women came to significant clinical harm or suffered a serious adverse clinical event. The two-step protocol incorporating the M6 model is an effective and clinically safe way of rationalizing the management of women with a PUL. Copyright © 2019 ISUOG. Published by John Wiley & Sons Ltd. [ABSTRACT FROM AUTHOR]

Rationale, aims and objectives Theories of decision making are divided between those aiming to help decision makers in the real, 'large' world and those who study decisions in idealized 'small' world settings. For the most part, these large- and small-world decision theories remain disconnected. Methods We linked the small-world decision theoretic concepts of signal detection theory ( SDT) and evidence accumulation theory ( EAT) to the threshold model and the large world of heuristic decision making that rely on fast-and-frugal decision trees ( FFT). Results We connected these large- and small-world theories by demonstrating that seemingly different decision-making concepts are actually equivalent. In doing so, we were able (1) to link the threshold model to EAT and FFT, thereby creating decision criteria that take into account both the classification accuracy of FFT and the consequences built in the threshold model; (2) to demonstrate how threshold criteria can be used as a strategy for optimal selection of cues when constructing FFT; and (3) to show that the compensatory strategy expressed in the threshold model can be linked to a non-compensatory FFT approach to decision making. We also showed how construction and performance of FFT depend on having reliable information - the results were highly sensitive to the estimates of benefits and harms of health interventions. We illustrate the practical usefulness of our analysis by describing an FFT we developed for prescribing statins for primary prevention of cardiovascular disease. Conclusions By linking SDT and EAT to the compensatory threshold model and to non-compensatory heuristic decision making ( FFT), we showed how these two decision strategies are ultimately linked within a broader theoretical framework and thereby respond to calls for integrating decision theory paradigms. [ABSTRACT FROM AUTHOR]

The development of molecular diagnostic tools to achieve individualized medicine requires accurate estimation of individual treatment effects (ITEs). Although several effective data analytic strategies have been proposed for this purpose, they have limitations when it comes to flexibly capturing the complex relationships between clinical outcome and possibly high-dimensional covariates. In this article, we propose an effective machine learning method to estimate ITEs using the gradient boosting trees (GBT). GBT is a powerful nonparametric regression tool in machine learning, and its outstanding performance has been widely recognized for various applications. We use GBT to develop an estimation method for the ITE that is formulated under the potential outcome model framework. Our method can flexibly capture the relationship between clinical outcome and possibly high-dimensional covariates, and it would also be useful for identifying subpopulations of patients who would benefit from the treatment. Results of simulation studies and a real-data analysis of a breast cancer clinical study show that the proposed method can precisely estimate ITEs, and these estimates possibly identify the subgroup of patients who can benefit from treatment. [ABSTRACT FROM AUTHOR]

Objective: Currently, depression diagnosis relies primarily on behavioral symptoms and signs, and treatment is guided by trial and error instead of evaluating associated underlying brain characteristics. Unlike past studies, we attempted to estimate accurate prediction models for late-life depression diagnosis and treatment response using multiple machine learning methods with inputs of multi-modal imaging and non-imaging whole brain and network-based features.Methods: Late-life depression patients (medicated post-recruitment) (n = 33) and older non-depressed individuals (n = 35) were recruited. Their demographics and cognitive ability scores were recorded, and brain characteristics were acquired using multi-modal magnetic resonance imaging pretreatment. Linear and nonlinear learning methods were tested for estimating accurate prediction models.Results: A learning method called alternating decision trees estimated the most accurate prediction models for late-life depression diagnosis (87.27% accuracy) and treatment response (89.47% accuracy). The diagnosis model included measures of age, Mini-mental state examination score, and structural imaging (e.g. whole brain atrophy and global white mater hyperintensity burden). The treatment response model included measures of structural and functional connectivity.Conclusions: Combinations of multi-modal imaging and/or non-imaging measures may help better predict late-life depression diagnosis and treatment response. As a preliminary observation, we speculate that the results may also suggest that different underlying brain characteristics defined by multi-modal imaging measures-rather than region-based differences-are associated with depression versus depression recovery because to our knowledge this is the first depression study to accurately predict both using the same approach. These findings may help better understand late-life depression and identify preliminary steps toward personalized late-life depression treatment. [ABSTRACT FROM AUTHOR]

This study examined how age of nonsuicidal self-injury (NSSI) onset relates to NSSI severity and suicidality using decision tree analyses (nonparametric regression models that recursively partition predictor variables to create groupings). Those with an earlier age of NSSI onset reported greater NSSI frequency, NSSI methods, and NSSI-related hospital visits. No significant splits were found for suicide ideation or attempts, although those with an earlier onset were more likely to have a suicide plan. Overall, findings suggest that onset of NSSI before age 12 is associated with more severe NSSI and may be a crucial age for prevention efforts. [ABSTRACT FROM AUTHOR]

Objective: To evaluate the cost-effectiveness of high-throughput, non-invasive prenatal testing (HT-NIPT) for fetal Rhesus D (RhD) genotype to guide antenatal prophylaxis with anti-D immunoglobulin compared with routine antenatal anti-D immunoglobulin prophylaxis (RAADP).Design: Cost-effectiveness decision-analytic modelling.Setting: Primary care.Participants: A simulated population of 100 000 RhD-negative women not known to be sensitised to the RhD antigen.Methods: A decision tree model was used to characterise the antenatal care pathway in England and the long-term consequences of sensitisation events. The diagnostic accuracy of HT-NIPT was derived from a systematic review and bivariate meta-analysis; estimates of other inputs were derived from relevant literature sources and databases. Women in whom the HT-NIPT was positive or inconclusive continued to receive RAADP, whereas women with a negative result received none. Five alternative strategies in which the use of HT-NIPT may affect the existing postpartum care pathway were considered.Main Outcome Measures: Costs expressed in 2015GBP and impact on health outcomes expressed in terms of quality-adjusted life-years over a lifetime.Results: The results suggested that HT-NIPT appears cost saving but also less effective than current practice, irrespective of the postpartum strategy evaluated. A postpartum strategy in which inconclusive test results are distinguished from positive results performed best. HT-NIPT is only cost-effective when the overall test cost is £26.60 or less.Conclusions: HT-NIPT would reduce unnecessary treatment with routine anti-D immunoglobulin and is cost saving when compared with current practice. The extent of any savings and cost-effectiveness is sensitive to the overall test cost.Tweetable Abstract: HT-NIPT is cost saving compared with providing anti-D to all RhD-negative pregnant women. [ABSTRACT FROM AUTHOR]

Aim: Data mining enables further insights from nutrition ‐ related research, but caution is required. The aim of this analysis was to demonstrate and compare the utility of data mining methods in classifying a categorical outcome derived from a nutrition ‐ related intervention. Methods: Baseline data (23 variables, 8 categorical) on participants (n = 295) in an intervention trial were used to classify participants in terms of meeting the criteria of achieving 10 000 steps per day. Results from classification and regression trees (CARTs), random forests, adaptive boosting, logistic regression, support vector machines and neural networks were compared using area under the curve (AUC) and error assessments. Results: The CART produced the best model when considering the AUC (0.703), overall error (18%) and within class error (28%). Logistic regression also performed reasonably well compared to the other models (AUC 0.675, overall error 23%, within class error 36%). All the methods gave different rankings of variables’ importance. CART found that body fat, quality of life using the SF ‐ 12 Physical Component Summary (PCS) and the cholesterol: HDL ratio were the most important predictors of meeting the 10 000 steps criteria, while logistic regression showed the SF ‐ 12PCS, glucose levels and level of education to be the most significant predictors (P ≤ 0.01). Conclusions: Differing outcomes suggest caution is required with a single data mining method, particularly in a dataset with nonlinear relationships and outliers and when exploring relationships that were not the primary outcomes of the research. [ABSTRACT FROM AUTHOR]

Adequate calcium intake during pregnancy is important in the prevention of pre-eclampsia. A substantial proportion of pregnant women do not meet the recommended daily calcium intake, even in developed countries. Nonetheless, calcium supplementation is not routinely advised to pregnant women in most countries. We aimed to predict the impact of advising pregnant women to use calcium supplements (1,000 mg/day) on the number of cases of pre-eclampsia prevented and related health care costs. By use of a decision-analytic model, we assessed the expected impact of advising calcium supplementation to either (1) all pregnant women, (2) women at high risk of developing pre-eclampsia, or (3) women with a low dietary calcium intake compared with current care. Calculations were performed for a hypothetical cohort of 100,000 pregnant women living in a high-income country, although input parameters of the model can be adjusted so as to fit other settings. The incidence of pre-eclampsia could be reduced by 25%, 8%, or 13% when advising calcium supplementation to all pregnant women, women at high risk of pre-eclampsia, or women with a low dietary calcium intake, respectively. Expected net financial benefits of the three scenarios were of €4,621,465, €2,059,165, or €2,822,115 per 100,000 pregnant women, respectively. Advising pregnant women to use calcium supplements can be expected to cause substantial reductions in the incidence of pre-eclampsia as well as related health care costs. It appears most efficient to advise calcium supplementation to all pregnant women, not subgroups only. [ABSTRACT FROM AUTHOR]

Aim: To identify the factors that prevent patients in respiratory failure from using noninvasive positive pressure ventilation (NPPV). The following were evaluated: (i) the patients who converted from NPPV to endotracheal intubation; and (ii) the patients who abandoned NPPV. Methods: Patients were interviewed regarding their experience with NPPV. Next, the factors that prevented the continuation of NPPV were identified and those data were collected retrospectively from medical records. The participants included adult patients in intensive care who were undergoing NPPV. The data from the interviews of nine participants were analyzed by using content analysis. Data that were collected from the medical records of 126 participants contributed to the identification of the characteristics that affected the implementation of NPPV. The factors were entered into a model by using logistic regression and decision-tree analysis. Results: An interview content analysis revealed eight aspects of the patients' experiences. In the medical record analysis, the specific factors that were associated with the conversion to intubation were the Acute Physiology and Chronic Health Evaluation II scores, breathing becoming easier, arterial oxygen partial pressure to fractional inspired oxygen ratio, and realizing the necessity of NPPV. The factors that were associated with the abandonment of NPPV included sleep loss, delirium, discomfort, and the arterial oxygen partial pressure to fractional inspired oxygen ratio. Realizing the necessity of NPPV was not statistically significant. With regard to the decision-tree analysis, the factors that were selected were similar to those that were selected in the factor analysis. Conclusion: In the initial stage of NPPV, focusing on dyspnea, sleep loss, discomfort, delirium, and the realization of the necessity of NPPV were critical in deciding on continuing this intervention or the early conversion to intubation. [ABSTRACT FROM AUTHOR]

Many large digital collections are currently organized by subject; although useful, these information organization structures are large and complex and thus difficult to browse. Current online tools and visualization prototypes show small, localized subsets and do not provide the ability to explore the predominant patterns of the overall subject structure. This study describes subject tree modifications that facilitate browsing for documents by capitalizing on the highly uneven distribution of real-world collections. The approach is demonstrated on two large collections organized by the Library of Congress Subject Headings ( LCSH) and Medical Subject Headings ( MeSH). Results show that the LCSH subject tree can be reduced to 49% of its initial complexity while maintaining access to 83% of the collection, and the MeSH tree can be reduced to 45% of its initial complexity while maintaining access to 97% of the collection. A simple solution to negate the loss of access is discussed. The visual impact is demonstrated by using traditional outline views and a slider control allowing searchers to change the subject structure dynamically according to their needs. This study has implications for the development of information organization theory and human-information interaction techniques for subject trees. [ABSTRACT FROM AUTHOR]

Background With growing interest in resilience among mental health care providers globally, there is a need for a simple way to consider the complex interactions that predict adaptive coping when there is exposure to high levels of adversity such as family violence, mental illness of a child or caregiver, natural disasters, social marginalization, or political conflict. Methods This article presents diagnostic criteria for assessing childhood resilience in a way that is sensitive to the systemic factors that influence a child's wellbeing. The most important characteristics of children who cope well under adversity and avoid problems like depression, PTSD, and delinquency are highlighted. Results A multidimensional assessment of resilience is presented that examines, first, the severity, chronicity, ecological level, children's attributions of causality, and cultural and contextual relevance of experiences of adversity. Second, promotive and protective factors related to resilience are assessed with sensitivity to the differential impact these have on outcomes depending on a child's level of exposure to adversity. These factors include individual qualities like temperament, personality, and cognitions, as well as contextual dimensions of positive functioning related to the available and accessibility of resources, their strategic use, positive reinforcement by a child's significant others, and the adaptive capacity of the environment itself. Third, an assessment of resilience includes temporal and cultural factors that increase or decrease the influence of protective factors. A decision tree for the diagnosis of resilience is presented, followed by a case study and diagnosis of a 15-year-old boy who required treatment for a number of mental health challenges. Conclusions The diagnostic criteria for assessing resilience and its application to clinical practice demonstrate the potential usefulness of a systemic approach to understanding resilience among child populations. [ABSTRACT FROM AUTHOR]

The federal government has encouraged open access to publicly funded federal science research results, but it is unclear what knowledge can be gleaned from them and how the knowledge can be used to improve scientific research and shape federal research policies. In this article, we present the results of a preliminary study of cyberlearning projects funded by the National Science Foundation ( NSF) that address these issues. Our work demonstrates that text-mining tools can be used to partially automate the process of finding NSF's cyberlearning awards and characterizing the fine-grained topics implicit in award abstracts. The methodology we have established to assess NSF's cyberlearning investments should generalize to other areas of research and other repositories of public-access documents. [ABSTRACT FROM AUTHOR]

Rationale To support doctors in diagnosing patients who are suspected to have pulmonary embolism, the Christopher evidence-based decision rule was implemented in hospitals in the Netherlands. This study examines whether the Christopher evidence-based decision rule is applied in clinical practice. In addition, doctors' considerations for not applying the decision rule are explored. Method Dyspnoea patients were included in the study prospectively. The diagnostic process of the patients with suspected pulmonary embolism, as judged by the treating doctor, was compared with the Christopher evidence-based decision rule using patient record reviews. In addition, 14 interviews were conducted with doctors who did not follow the Christopher evidence-based decision rule to obtain insights into their considerations. Results In 80 of 247 dyspnoea cases, the treating doctors suspected pulmonary embolism. The Christopher evidence-based decision rule was applied in 17 out of 80 cases. In 22 cases, more tests were performed than was suggested by the decision rule [i.e. computer-assisted tomographic angiography ( CTa) or d-dimer], while in 41 cases fewer tests were performed than suggested by the decision rule. Considerations for not following the decision rule included judging another diagnosis to be more likely and not wanting to expose the patient to CTa radiation. Conclusions The Christopher evidence-based decision rule for diagnosing pulmonary embolism was not always followed in everyday clinical practice. Doctors seem to base their diagnostic strategy on their own estimate of the likelihood of pulmonary embolism, rather than the whole decision rule. Better adherence to the decision rule could be beneficial by making doctors aware that pulmonary embolism is more likely than they initially thought. However, in a substantial number of cases, it seemed justifiable that doctors deviated from the decision rule. Therefore, further research is needed to determine the value of the Christopher evidence-based decision rule in clinical practice. [ABSTRACT FROM AUTHOR]

Rationale Little scrutiny has been applied to how 'cost per fall' values have been calculated and applied. This paper presents discourse discussing how the cost of fall statistic could potentially be misleading when applied to inpatient health or residential care settings and provides estimates of the cost of falls and cost of falls per person. Method Burden of disease was estimated using a decision tree approach. Data informing the decision tree were drawn from a retrospective audit of 545 falls in a residential care facility, a visual observation study of 46 residents from the same facility and a cohort study of 186 residents from nine different facilities in Australia. Acute care and transportation costs were extracted from the retrospective audit using incident reports and care note review. The distribution of falls per person and associations between falls, fractures and change in resident mobility were extracted from the cohort study. The association between resident mobility and the amount of time required to perform toileting, transfer and dressing activities was extracted from the visual observational study. Results The minimum 'cost per fall' was estimated to be $AUD 841 and the maximum was $AUD 1024. The 'cost of falls per person' estimate was $AUD 1887 (2008 base year). Conclusions This cost per fall estimate was substantially lower than three previous internationally derived estimates possibly as a consequence of how fall data were collected and modelled in these studies. Cost of falls per person may be a preferable statistic for future use. [ABSTRACT FROM AUTHOR]

Introduction: Research has been conducted over several years to develop a new off-road assessment battery referred to as the Occupational Therapy - Driver Off-Road Assessment Battery. This article documents the development of the Battery, and provides preliminary research evidence to support its content and predictive validity. Methods: Literature reviews and a focus group with nine driver assessor occupational therapists were undertaken, as well as data collection using the Occupational Therapy - Driver Off-Road Assessment Battery with 246 clients. A Classification and Regression Tree model was constructed to ascertain the predictive validity of the Battery, with fitness-to-drive as the outcome. Results: Twenty-one physical, 13 sensory and seven assessments of cognition/perception were identified as being reflective of the skills required for driving. Following rating of their psychometric properties, the best assessments were presented to focus group members. The driver assessors supported the inclusion of several assessments and encouraged the development of new assessments. A draft version of the Occupational Therapy - Driver Off-Road Assessment Battery was tested and found to have excellent predictive validity for client on-road performance of 82.6%. The Classification and Regression Tree model showed that client performance on tests included in the Battery should be used together, rather than in isolation, to support fitness-to-drive recommendations. Conclusion: This research identified the most suitable physical, sensory and cognitive assessments to include in the Occupational Therapy - Driver Off-Road Assessment Battery, and provided support for its validity. The development of this standardised battery assists driver assessors to accurately and consistently assess and report the off-road driving capacity of clients. [ABSTRACT FROM AUTHOR]

Background Critical appraisal, one of the most crucial steps in the practice of evidence-based medicine, is expertise-dependent and time-consuming. The objective of this study was to develop and evaluate an automated text-mining system that could determine the evidence level provided by a medical article. Methods A text processor was designed and built to interpret the abstracts of medical literature. The system extracted information about: (1) the impact factor of the journal; (2) study design; (3) human subject involvement; (4) number of subjects; (5) P-value; and (6) confidence intervals. We used a classification tree algorithm (C4.5) to create a decision tree using supervised classification. Each article was categorized into evidence level A, B or C, and the output was compared to that determined by domain experts (the reference standard). Results We used a corpus of 3180 cardiovascular disease original research articles, of which 1108 were previously assigned evidence level A, 1705 level B and 367 level C by domain experts. The abstracts were analysed by our automated system and an evidence level was assigned. The algorithm accurately classified 85% of the articles. The agreement between computer and domain experts was substantial ( κ-value: 0.78). Cross-validation showed consistent results across repeated tests. Conclusion The automated engine accurately classified the evidence level. Misclassification might have resulted from incomplete information retrieval and inaccurate data extraction. Further efforts will focus on assessing relevance and using additional study design features to refine evidence level classification. [ABSTRACT FROM AUTHOR]

Map of Medicine is an evidence-based online clinical knowledge resource. Procured at significant cost by healthcare providers in the UK, Sweden and Denmark, it is important to establish the beneficial impact that investment has had on healthcare practise and, ultimately, on patient outcomes. The objective of this study is to review the evidence for the impact of the Map of Medicine on clinical practice. A systematic review of peer-reviewed and grey literature was conducted. Nine healthcare databases, Google Scholar and Google were searched for articles containing the terms 'map of medicine'. The search identified 133 articles. Eleven of the articles identified met the inclusion criteria. The main finding of the study is the paucity of evidence available on the impact of the Map of Medicine and the variable quality of that which does exist. There are some encouraging early indications for the Map of Medicine as a tool within service redesign, leading to an increase in appropriate referrals to secondary care, decreased patient waiting times and considerable cost savings. Further research with study designs that can generate high quality evidence for the impact of Map of Medicine is essential in order to support policy decisions. [ABSTRACT FROM AUTHOR]

Background and Aims: Participating in online gambling is associated with an increased risk for experiencing gambling‐related harms, driving calls for more effective, personalized harm prevention initiatives. Such initiatives depend on the development of models capable of detecting at‐risk online gamblers. We aimed to determine whether machine learning algorithms can use site data to detect retrospectively at‐risk online gamblers indicated by the Problem Gambling Severity Index (PGSI). Design: Exploratory comparison of six prominent supervised machine learning methods (decision trees, random forests, K‐nearest neighbours, logistic regressions, artificial neural networks and support vector machines) to predict problem gambling risk levels reported on the PGSI. Setting: Lotoquebec.com (formerly espacejeux.com), an online gambling platform operated by Loto‐Québec (a provincial Crown Corporation) in Quebec, Canada. Participants: N = 9145 adults (18+) who completed the survey measure and placed at least one bet using real money on the site. Measurements Participants completed the PGSI, a self‐report questionnaire with validated cut‐offs denoting a moderate‐to‐high‐risk (PGSI 5+) or high‐risk (PGSI 8+) for experiencing past‐year gambling‐related problems. Participants agreed to release additional data about the preceding 12 months from their user accounts. Predictor variables (144) were derived from users' transactions, apparent betting behaviours, listed demographics and use of responsible gambling tools on the platform. Findings Our best classification models (random forests) for the PGSI 5+ and 8+ outcome variables accounted for 84.33% (95% CI = 82.24–86.41) and 82.52% (95% CI = 79.96–85.08) of the total area under their receiver operating characteristic curves, respectively. The most important factors in these models included the frequency and variability of participants' betting behaviour and repeat engagement on the site. Conclusions: Machine learning algorithms appear to be able to classify at‐risk online gamblers using data generated from their use of online gambling platforms. They may enable personalized harm prevention initiatives, but are constrained by trade‐offs between their sensitivity and precision. [ABSTRACT FROM AUTHOR]

Purpose: Pain management within emergency departments (ED) remains challenging. Given that unrelieved pain in children is linked to a number of negative physiological and psychological consequences, optimal management of children's pain is paramount. Many studies exploring children's pain have adopted quantitative methods or sought the perspectives of adults. Compared to adults, studies examining children's views on pain and pain management are limited. This study aimed to explore children's pain experiences, their perception of pain management and expectations of the role of the nurse. Design: This was a qualitative descriptive study using an inductive approach. Methods: Fifteen children, aged 4–8 years who presented to the ED of an Australian tertiary pediatric hospital in acute pain participated. Data were collected using draw, write, and tell (DWT) technique and analyzed using thematic analysis. Results: Three themes emerged (1) "Security," (2) "My pain" with subthemes: "The pain feelings" and "My sad/happy feelings," (3) "Comfort and relief" with subthemes: "Taking my mind off it," "Resting" and "Hospital things." When in pain children needed to feel secure. Parents and nurses were important in fostering a secure environment for children. Children were capable of describing their pain and identified nonpharmacological strategies to help their pain. Practice implications: Children as young as 4 years old can provide detailed accounts of their pain, which extends beyond physical dimensions to include visual, auditory, and sensory features. Nurses need to listen, be honest, and develop trust with children to be helpful. Nonpharmacological pain‐relieving strategies can be implemented by parents and nurses in collaboration with the child. Fostering a secure environment is essential. [ABSTRACT FROM AUTHOR]

Background: MYCN gene amplification (MNA) is a hallmark of aggressive neuroblastoma. This study was performed to determine univariate and multivariate predictors of tumor MNA. Methods: Data from the International Neuroblastoma Risk Group were analyzed for a subset of 7102 patients with known MYCN status. Chi-square testing and logistic regression were used to identify univariate and multivariate predictors of MYCN status. Recursive partitioning was used to identify groups of patients with maximal differences in rates of MNA. Results: All clinical features (age ≥ 18 months, high ferritin levels, high lactate dehydrogenase [LDH] levels, International Neuroblastoma Staging System stage 4, and adrenal sites) and pathological/biological features (DNA index ≤ 1, high mitosis-karyorrhexis index [MKI], undifferentiated/poorly differentiated grade, unfavorable histology according to the International Neuroblastoma Pathology Classification, and segmental chromosomal aberrations [SCAs]) were significantly associated with MNA. LDH (odds ratio [OR], 8.4; P < .001) and chromosomal 1p loss of heterozygosity (OR, 19.8; P < .001) were the clinical and biological variables, respectively, most strongly associated with MNA. In logistic regression, all variables except chromosome 17q aberration and pooled SCAs were independently predictive of MNA. Recursive partitioning identified subgroups with disparate rates of MNA, including subgroups with 85.7% MNA (patients with high LDH levels who had poorly differentiated adrenal tumors with chromosome 1p deletion) and 0.6% MNA (localized tumors having hyperdiploidy and low MKIs and lacking chromosome 1p aberrations). Conclusions: MNA is strongly associated with other clinical and biological variables in neuroblastoma. Recursive partitioning has identified subgroups of neuroblastoma patients with highly disparate rates of MNA. These findings can be used to inform investigations of molecular mechanisms of MNA. [ABSTRACT FROM AUTHOR]

Background: Study objectives were 1) to estimate diagnostic capacity of clinical parameters, receptor activator of nuclear factor kappa-B (RANKL) and osteoprotegerin (OPG) to diagnose healthy peri-implant condition (HI), peri-implant mucositis (PIM) and peri-implantitis (PIMP) by assessing respective diagnostic accuracy, sensitivity, specificity and diagnostic ranges 2) to develop personalized diagnostic model (PDM) for implant monitoring.Methods: Split-mouth study included 126 patients and 252 implants (HI = 126, PIM = 57, and PIMP = 69). RANKL and OPG concentrations were estimated in peri-implant crevicular fluid using enzyme-linked immunosorbent assay method and assessed with clinical parameters using routine statistics, while the diagnostic capacity of individual parameters and overall clinical diagnosis were estimated using classifying algorithms. PDM was developed using decision trees.Results: Bleeding on probing (BOP), plaque index, and probing depth (PD) were confirmed reliable discriminants between peri-implant health and disease, while increase in PD (PD > 4 mm) and suppuration were good discriminants amongst PIM/PIMP. Bone turnover markers (BTMs) demonstrated presence of bone resorption in PIM; between comparable diagnostic ranges PIM/PIMP, PIMP was clinically distinguished from PIM in about 60% of patients while 40% remained diagnosed as false negatives. PDM demonstrated highest diagnostic capacity (accuracy: 96.27%, sensitivity: 95.00%, specificity: 100%) and defined HI: BOP ≤0.25%; PIM: BOP >0.25%, PD ≤4.5 mm; PIMP: BOP >0.25%, PD >4.5 mm and RANKL ≤19.9 pg/site; PIM: BOP >0.25%, PD >4.5 mm, and RANKL >19.9 pg/site.Conclusions: BTMs demonstrated capacity to substantially improve clinical diagnosis of peri-implant conditions. Considering lack of difference in BTMs between PIM/PIMP and cluster of PIM with exceeding BTMs, a more refined definition of peri-implant conditions according to biological characteristics is required for further BTMs validation and appropriate PIMP management. [ABSTRACT FROM AUTHOR]