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We propose to combine the benefits of flexible parametric survival modeling and regularization to improve risk prediction modeling in the context of time‐to‐event data. Thereto, we introduce ridge, lasso, elastic net, and group lasso penalties for both log hazard and log cumulative hazard models. The log (cumulative) hazard in these models is represented by a flexible function of time that may depend on the covariates (i.e., covariate effects may be time‐varying). We show that the optimization problem for the proposed models can be formulated as a convex optimization problem and provide a user‐friendly R implementation for model fitting and penalty parameter selection based on cross‐validation. Simulation study results show the advantage of regularization in terms of increased out‐of‐sample prediction accuracy and improved calibration and discrimination of predicted survival probabilities, especially when sample size was relatively small with respect to model complexity. An applied example illustrates the proposed methods. In summary, our work provides both a foundation for and an easily accessible implementation of regularized parametric survival modeling and suggests that it improves out‐of‐sample prediction performance. [ABSTRACT FROM AUTHOR]

Risk prediction models fitted using maximum likelihood estimation (MLE) are often overfitted resulting in predictions that are too extreme and a calibration slope (CS) less than 1. Penalized methods, such as Ridge and Lasso, have been suggested as a solution to this problem as they tend to shrink regression coefficients toward zero, resulting in predictions closer to the average. The amount of shrinkage is regulated by a tuning parameter, λ , $\lambda ,$ commonly selected via cross-validation ("standard tuning"). Though penalized methods have been found to improve calibration on average, they often over-shrink and exhibit large variability in the selected λ $\lambda $ and hence the CS. This is a problem, particularly for small sample sizes, but also when using sample sizes recommended to control overfitting. We consider whether these problems are partly due to selecting λ $\lambda $ using cross-validation with "training" datasets of reduced size compared to the original development sample, resulting in an over-estimation of λ $\lambda $ and, hence, excessive shrinkage. We propose a modified cross-validation tuning method ("modified tuning"), which estimates λ $\lambda $ from a pseudo-development dataset obtained via bootstrapping from the original dataset, albeit of larger size, such that the resulting cross-validation training datasets are of the same size as the original dataset. Modified tuning can be easily implemented in standard software and is closely related to bootstrap selection of the tuning parameter ("bootstrap tuning"). We evaluated modified and bootstrap tuning for Ridge and Lasso in simulated and real data using recommended sample sizes, and sizes slightly lower and higher. They substantially improved the selection of λ $\lambda $ , resulting in improved CS compared to the standard tuning method. They also improved predictions compared to MLE., (© 2024 The Author(s). Biometrical Journal published by Wiley‐VCH GmbH.)

In psychological studies, multivariate outcomes measured on the same individuals are often encountered. Effects originating from these outcomes are consequently dependent. Multivariate meta-analysis examines the relationships of multivariate outcomes by estimating the mean effects and their variance-covariance matrices from series of primary studies. In this paper we discuss a unified modelling framework for multivariate meta-analysis that also incorporates measurement error corrections. We focus on two types of effect sizes, standardized mean differences (d) and correlations (r), that are common in psychological studies. Using generalized least squares estimation, we outline estimated mean vectors and variance-covariance matrices for d and r that are corrected for measurement error. Given the burgeoning research involving multivariate outcomes, and the largely overlooked ramifications of measurement error, we advocate addressing measurement error while conducting multivariate meta-analysis to enhance the replicability of psychological research., (© 2024 The Authors. British Journal of Mathematical and Statistical Psychology published by John Wiley & Sons Ltd on behalf of British Psychological Society.)

The rising popularity of artificial intelligence in healthcare is highlighting the problem that a computational model achieving super-human clinical performance at its training sites may perform substantially worse at new sites. In this perspective, we argue that we should typically expect this failure to transport, and we present common sources for it, divided into those under the control of the experimenter and those inherent to the clinical data-generating process. Of the inherent sources we look a little deeper into site-specific clinical practices that can affect the data distribution, and propose a potential solution intended to isolate the imprint of those practices on the data from the patterns of disease cause and effect that are the usual target of probabilistic clinical models., (© 2024. The Author(s).)

Lower extremity open revascularization is a treatment option for peripheral artery disease that carries significant peri-operative risks; however, outcome prediction tools remain limited. Using machine learning (ML), we developed automated algorithms that predict 30-day outcomes following lower extremity open revascularization. The National Surgical Quality Improvement Program targeted vascular database was used to identify patients who underwent lower extremity open revascularization for chronic atherosclerotic disease between 2011 and 2021. Input features included 37 pre-operative demographic/clinical variables. The primary outcome was 30-day major adverse limb event (MALE; composite of untreated loss of patency, major reintervention, or major amputation) or death. Our data were split into training (70%) and test (30%) sets. Using tenfold cross-validation, we trained 6 ML models. Overall, 24,309 patients were included. The primary outcome of 30-day MALE or death occurred in 2349 (9.3%) patients. Our best performing prediction model was XGBoost, achieving an area under the receiver operating characteristic curve (95% CI) of 0.93 (0.92-0.94). The calibration plot showed good agreement between predicted and observed event probabilities with a Brier score of 0.08. Our ML algorithm has potential for important utility in guiding risk mitigation strategies for patients being considered for lower extremity open revascularization to improve outcomes., (© 2024. The Author(s).)

Purpose: Work-related musculoskeletal disorders (WRMD) are the most common causes of disability worldwide and are associated with significant use of healthcare. One way to optimize the clinical outcomes of injured workers receiving rehabilitation is to identify and address individual prognostic factors (PF), which can facilitate the personalization of the treatment plan. As there is no pragmatic and systematic method to collect prognostic-related data, the purpose of the study was to develop and assess the acceptability of a set of questionnaires to establish the "prognostic profile" of workers with WRMD., Methods: We utilized a multistep process to inform the acceptability of the Measures Associated to PrognoStic (MAPS) questionnaire. During STEP-1, a preliminary version of the was developed through a literature search followed by an expert consensus including a patient-advisor. During STEP-2, future users (rehabilitation professionals, healthcare administrators and compensation officers) were consulted through an online survey and were asked to rate the relevance of each content item; items that obtained ≥80% of "totally agree" answers were included. They were also asked to prioritize PF according to their usefulness for clinical decision-making, as well as perceived efficacy to enhance the treatment plan., Results: The questionnaire was developed with three categories: the outcome predicted, the unique PF, and prognostic tools. Personal PF (i.e.: coping strategies, fear-avoidance beliefs), pain related PF (i.e.: pain intensity/severity, duration of pain), and work-related PF (i.e.: work physical demands, work accommodations) were identified to be totally relevant and included in the questionnaire. 84% of the respondents agreed that their patients could complete the MAPS questionnaire in their clinical setting, while 75% totally agreed that the questionnaire is useful to personalize rehabilitation interventions., Conclusion: The MAPS questionnaire was deemed acceptable to establish the "prognostic profile" of injured workers and help the clinicians in the treatment decision-making process., (© 2023 The Authors. Physiotherapy Research International published by John Wiley & Sons Ltd.)

Logistic regression is one of the most commonly used approaches to develop clinical risk prediction models. Developers of such models often rely on approaches that aim to minimize the risk of overfitting and improve predictive performance of the logistic model, such as through likelihood penalization and variance decomposition techniques. We present an extensive simulation study that compares the out-of-sample predictive performance of risk prediction models derived using the elastic net, with Lasso and ridge as special cases, and variance decomposition techniques, namely, incomplete principal component regression and incomplete partial least squares regression. We varied the expected events per variable, event fraction, number of candidate predictors, presence of noise predictors, and the presence of sparse predictors in a full-factorial design. Predictive performance was compared on measures of discrimination, calibration, and prediction error. Simulation metamodels were derived to explain the performance differences within model derivation approaches. Our results indicate that, on average, prediction models developed using penalization and variance decomposition approaches outperform models developed using ordinary maximum likelihood estimation, with penalization approaches being consistently superior over the variance decomposition approaches. Differences in performance were most pronounced on the calibration of the model. Performance differences regarding prediction error and concordance statistic outcomes were often small between approaches. The use of likelihood penalization and variance decomposition techniques methods was illustrated in the context of peripheral arterial disease., (© 2023 The Authors. Biometrical Journal published by Wiley-VCH GmbH.)

Comparative simulation studies are workhorse tools for benchmarking statistical methods. As with other empirical studies, the success of simulation studies hinges on the quality of their design, execution, and reporting. If not conducted carefully and transparently, their conclusions may be misleading. In this paper, we discuss various questionable research practices, which may impact the validity of simulation studies, some of which cannot be detected or prevented by the current publication process in statistics journals. To illustrate our point, we invent a novel prediction method with no expected performance gain and benchmark it in a preregistered comparative simulation study. We show how easy it is to make the method appear superior over well-established competitor methods if questionable research practices are employed. Finally, we provide concrete suggestions for researchers, reviewers, and other academic stakeholders for improving the methodological quality of comparative simulation studies, such as preregistering simulation protocols, incentivizing neutral simulation studies, and code and data sharing., (© 2023 The Authors. Biometrical Journal published by Wiley-VCH GmbH.)

Background: β-blockers have been widely used in patients with extensive cardiovascular disease (CVD) and have provided benefits. However, they are more likely to cause symptomatic bradycardia, hypotension, or glucose metabolism disorders, which may lead to an increased risk of atrial fibrillation (AF), but evidence is lacking. Aims: This study was to analyze the association between the use of β-blockers and the risk of developing AF. Methods: This nationwide, prospective cohort study utilized data from the 2013--2020 National Health and Nutrition Examination Survey (NHANES). The patients were stratified into a β-blocker treatment group (n = 2585) and a non-β-blocker treatment group (n = 8525). Univariate and multivariate logistic regression analyses were performed to identify the relationship between β-blockades and the risk of AF. Propensity matching analysis was used to balance patient baseline characteristics and to control for confounders. Results: A total of 11,110 subjects were included in this study (mean [SD] age, 59.89 [15.07] years; 5657 [49.7%] males). A total of 111/2585 subjects developed AF in the β-blocker treatment group, and 75/8525 developed AF in the non-β-blocker treatment group (incidence rate, 4.2% vs. 0.8%). Compared with the non-β-blocker group, the β-blocker group had an increased risk of incident AF (aOR, 2.339; 95% CI, 1.614--3.410). Some sensitivity analyses also revealed consistent findings of increased AF risk associated with β-blocker treatment. Conclusion: The findings from this study suggest that β-blocker treatment is associated with an increased risk of incident AF and may help physicians select a modest medication for patients while also assessing the risk of AF. [ABSTRACT FROM AUTHOR]

Data pooling is a powerful strategy in empirical research. However, combining multiple datasets often results in a large amount of missing data, as variables that are not present in some datasets effectively contain missing values for all participants in those datasets. Furthermore, data pooling typically leads to a mix of continuous and categorical items with nonnormal multivariate distributions. We investigated two popular approaches to handle missing data in this context: (1) applying direct maximum likelihood by treating data as continuous (con-ML), and (2) applying categorical least squares using a polychoric correlation matrix computed from pairwise deletion (cat-LS). These approaches are available for free and relatively straightforward for empirical researchers to implement. Through simulation studies with confirmatory factor analysis and latent mediation analysis, we found cat-LS to be unsuitable for pooled data analysis, whereas con-ML yielded acceptable performance for the estimation of latent path coefficients barring severe nonnormality. [ABSTRACT FROM AUTHOR]

Objective Bayesian inference procedures are derived for the parameters of the multivariate random effects model generalized to elliptically contoured distributions. The posterior for the overall mean vector and the between-study covariance matrix is deduced by assigning two noninformative priors to the model parameter, namely the Berger and Bernardo reference prior and the Jeffreys prior, whose analytical expressions are obtained under weak distributional assumptions. It is shown that the only condition needed for the posterior to be proper is that the sample size is larger than the dimension of the data-generating model, independently of the class of elliptically contoured distributions used in the definition of the generalized multivariate random effects model. The theoretical findings of the paper are applied to real data consisting of ten studies about the effectiveness of hypertension treatment for reducing blood pressure where the treatment effects on both the systolic blood pressure and diastolic blood pressure are investigated. [ABSTRACT FROM AUTHOR]

Accessible Summary: What is known on the subject?: People diagnosed with serious mental illness have a high risk of diabetes and are more likely to develop type 2 diabetes at ages below 60 years.Effective diabetes self‐care behaviours among people diagnosed with serious mental illness can improve glycaemic control and reduce vascular complications.Few studies have investigated diabetes self‐care behaviours and their associations with health literacy and self‐efficacy in people diagnosed with serious mental illness. What the paper adds to existing knowledge?: Diabetes self‐care behaviours in people diagnosed with serious mental illness were suboptimal; the least frequently performed self‐care activities were self‐monitoring of blood glucose.Factors associated with diabetes self‐care behaviours are gender, age, communicative and critical health literacy and self‐efficacy.Self‐efficacy is the strongest predictor of self‐care behaviours among people diagnosed with comorbid serious mental illness and type 2 diabetes. What are the implications of practice?: Mental health nurses should assess diabetes‐specific health literacy of people diagnosed with serious mental illness to ensure that they possess the knowledge and skills related to diabetes self‐care.When treating young people and those with newly diagnosed type 2 diabetes, nurses should incorporate strategies to minimise their perceptions of diabetes‐related distress and increase their confidence in managing comorbid diabetes. Introduction: People diagnosed with serious mental illness (SMI) experience greater challenges in managing their type 2 diabetes mellitus (T2DM) than do those diagnosed with T2DM alone. Aim: This study investigated diabetes self‐care activities and the factors associated with these activities in people diagnosed with SMI in a hospital setting. Methods: A cross‐sectional correlational study was conducted among 126 people diagnosed with comorbid SMI and T2DM in Taipei, Taiwan, between October 2020 and April 2021. Data were collected using self‐report questionnaires and a chart review. Three‐step hierarchical multiple regression analysis was used to identify factors associated with diabetes self‐care behaviours. Results: Diabetes self‐care behaviours in people diagnosed with SMI were suboptimal overall. Hierarchical multiple regression analyses revealed that age (β = 0.18, p =.037) and self‐efficacy (β = 0.27, p =.004) significantly associated with diabetes self‐care behaviours. Discussion: Self‐efficacy is the strongest predictor of self‐care behaviours among people diagnosed with comorbid SMI and T2DM. Implications for Practice: Mental health professionals should focus on enhancing confidence in managing comorbid diabetes in people diagnosed with comorbid SMI, especially young people and those with newly diagnosed T2DM. [ABSTRACT FROM AUTHOR]

Co-teaching and related collaborative models of instruction are widely used in primary and secondary schools in many school systems. This systematic review and meta-analysis investigated the effects of such models on students' academic achievement and how these effects are moderated by factors of theoretical and practical relevance. Although previous research and reviews have asserted that the evidence base is scarce, we found 128 treatment and control group studies from 1984 to 2020. We excluded 52 studies due to critical risk of bias via Cochrane's risk of bias assessment tools and conducted a meta-analysis of 76 studies. This yielded 280 short-term effect sizes, of which 82% were pretest-adjusted. We found a moderate, positive, and statistically significant mean effect of g ¯ =.11, 95% confidence interval [.035,.184] of collaborative instruction compared to single-taught controls, using the correlated-hierarchical effects (CHE-RVE) model. From moderator analyses, we found that collaborative instruction yields effects of mostly the same size, whether the interventions involved trained teachers or assistants with no teaching qualifications. This implies a potential for the expansion of such interventions at lower costs than otherwise expected. Moreover, factors that are highlighted in the co-teaching literature as preconditions for the effectiveness of collaborative instruction did not explain variations in effect sizes. Finally, we found no clear evidence for publication bias or small study effects. Notably, a large number of the studies that we drew upon were nonrandomized studies; and therefore, more rigorous experimental research is needed, especially on relevant co-teaching interventions. [ABSTRACT FROM AUTHOR]

Objective The random urine protein-to-creatinine ratio (UPCR) is a screening test used for predicting clinically significant proteinuria (urine protein ≥ 300 mg) during pregnancy. No consensus exists on the optimal random UPCR cutoff for performing follow-up 24 hour urine (24H) total protein collection. We aim to evaluate the test performance of random UPCR in predicting proteinuria in a contemporary cohort. Study Design This was a retrospective cohort study of pregnant patients at our institution from 2014 to 2018 with a random UPCR and follow-up 24H protein collection. The primary analysis estimated the test characteristics (sensitivity, specificity, positive and negative predictive values) of using random UPCR for the detection of proteinuria defined as urine protein ≥300 mg on 24H protein collection. UPCR cutoffs from 0.10 to 0.30 mg/dL were evaluated, receiver operator characteristic (ROC) curve was constructed, and area under the curve (AUC) was determined. A secondary analysis examined the correlation between UPCR and 24H protein using least squares regression and Pearson correlation. Results Paired UPCR and 24H collection results were available for 1,120 patients. Mean gestational age at time of UPCR was 31.1 ± 5.1 weeks and 687 (61.3%) of patients had a 24H ≥300 mg. UPCR <0.10 mg/dL effectively excluded proteinuria ≥300 mg on 24H collection, while UPCR ≥0.18 mg/dL correctly classifies proteinuria with 91% sensitivity, 57% specificity, 77% positive predictive value, and 79% negative predictive value. UPCR ≥1.07 mg/dL had 100% specificity for 24 hour proteinuria. The area under ROC curve was 0.86. UPCR and 24H collection were highly correlated with a Pearson correlation coefficient of 0.85. After our institution lowered the threshold to obtain a 24H from UPCR ≥0.20 mg/dL to ≥0.10 mg/dL in May 2017, the percentage of patients meeting criteria for 24H collection increased from 57.8 to 84.4%. Conclusion The AUC and Pearson correlation suggest random UPCR is a high performance test for the prediction of proteinuria on 24H. Optimal test performance is dependent upon clinical consideration and upon the implications of the disease or condition. A random UPCR screen positive threshold of 0.18 mg/dL maximizes sensitivity to identify clinically significant proteinuria. Key Points Random urine protein to creatinine ratio is a high performance test for proteinuria. A random UPCR threshold of 0.18 mg/dL maximizes sensitivity to identify proteinuria. Optimal test performance is dependent on the disease or clinical condition. [ABSTRACT FROM AUTHOR]

Introduction: Major trauma centre (MTC) care has been associated with improved outcomes for injured patients. English ambulance services and trauma networks currently use a range of triage tools to select patients for bypass to MTCs. A standardised national triage tool may improve triage accuracy, cost-effectiveness and the reproducibility of decision-making. Methods: We conducted an expert consensus process to derive and develop a major trauma triage tool for use in English trauma networks. A web-based Delphi survey was conducted to identify and confirm candidate triage tool predictors of major trauma. Facilitated roundtable consensus meetings were convened to confirm the proposed triage tool’s purpose, target diagnostic threshold, scope, intended population and structure, as well as the individual triage tool predictors and cut points. Public and patient involvement (PPI) focus groups were held to ensure triage tool acceptability to service users. Results: The Delphi survey reached consensus on nine triage variables in two domains, from 109 candidate variables after three rounds. Following a review of the relevant evidence during the consensus meetings, iterative rounds of discussion achieved consensus on the following aspects of the triage tool: reference standard, scope, target diagnostic accuracy and intended population. A three-step tool comprising physiology, anatomical injury and clinical judgement domains, with triage variables assessed in parallel, was recommended. The triage tool was received favourably by PPI focus groups. Conclusions: This paper presents a new expert consensus derived major trauma triage tool with defined purpose, scope, intended population, structure, constituent variables, variable definitions and thresholds. Prospective evaluation is required to determine clinical and cost-effectiveness, acceptability and usability. [ABSTRACT FROM AUTHOR]

We conducted a systematic review aiming to summarize the data on the current hemorrhage prediction models and evaluate their potential for generalized application in the neonatal population. The electronic databases PubMed and Scopus were searched, up to September 20, 2023, for studies that focused on development and/or validation of a prediction model for bleeding risk in neonates, and described the process of model building. Nineteen studies fulfilled the inclusion criteria for the present review. Eighteen bleeding risk prediction models in the neonatal population were identified, four of which were internally validated, one temporally and one externally validated. The existing prediction models for neonatal hemorrhage are mostly based on clinical variables and do not take into account the clinical course and hemostatic profile of the neonates. Most studies aimed at predicting the risk of intraventricular hemorrhage (IVH) reflecting the fact that IVH is the most frequent and serious bleeding complication in preterm neonates. A justification for the study sample size for developing the prediction model was given only by one study. Prediction and stratification of risk of hemorrhage in neonates is yet to be optimized. To this end, qualitative standards for model development need to be further improved. The assessment of the risk of bleeding incorporating platelet count, coagulation parameters, and a set of relevant clinical variables is crucial. Large, rigorous, collaborative cohort studies are warranted to develop a robust prediction model to inform the need for transfusion, which is a fundamental step towards personalized transfusion therapy in neonates. [ABSTRACT FROM AUTHOR]

The traditional approach to formulating building codes often is slow and labor-intensive, and may struggle to keep pace with the rapid evolution of technology and domain findings. Overcoming such challenges necessitates a methodology that streamlines the modernization of codal provisions. This paper proposes a machine learning (ML) approach to append a variety of codal provisions, including those of empirical, statistical, and theoretical natures. In this approach, a codal provision (i.e., equation) is analyzed to trace its properties (e.g., engineering intuition and causal logic). Then a ML model is tailored to preserve the same properties and satisfy a collection of similarity and performance measures until declared equivalent to the provision at hand. The resulting ML model harnesses the predictive capabilities of ML while arriving at predictions similar to the codal provision used to train the ML model, and hence it becomes possible to use in lieu of the codal expression. This approach was examined successfully for seven structural engineering phenomena contained within various building codes, including those in North America and Australia. The findings suggest that the proposed approach could lay the groundwork for implementing ML in the development of future building codes. [ABSTRACT FROM AUTHOR]

Gender differences in early childhood development are under-researched in low- and middle-income countries (LMICs) where almost 90% of the world's young children live. This study examines the direction, magnitude, and variation of gender similarities and differences in 3- and 4-year-olds' early childhood development in 71 low- and middle-income countries (LMICs). Moreover, we explore whether country-level gender inequality correlates with gender differences in early development. We analyzed Early Childhood Development Index (ECDI) data from UNICEF's Multiple Indicators Cluster Surveys and the Demographic and Health Surveys between 2010 and 2019 (n = 226,980). Using logistic regression, we estimated children's odds of being developmentally on track in literacy-numeracy, approaches to learning, social-emotional, and physical domains. We also used meta-analytic regression to examine associations between early childhood development and country-level indicators of gender inequality. In approximately one-half of LMICs, odds of being developmentally on track did not significantly differ for girls and boys; in countries with significant differences, girls were more likely to be on track than boys (odds ratio [OR] = 1.21, 95% confidence interval [CI] = [1.18–1.24], p <.001). Differences favoring girls were largest in the social-emotional domain (OR = 1.27, 95% CI = [1.24–1.30], p <.001). Country-level gender development index explained a substantial amount of variation in gender differences across countries. Overall, findings suggest that when gender differences exist, girls are more likely than boys to be developmentally on track, particularly in the social-emotional domain and in countries with greater gender equality. [ABSTRACT FROM AUTHOR]

Accurate prediction of recurrence and progression in non-muscle invasive bladder cancer (NMIBC) is essential to inform management and eligibility for clinical trials. Despite substantial interest in developing artificial intelligence (AI) applications in NMIBC, their clinical readiness remains unclear. This systematic review aimed to critically appraise AI studies predicting NMIBC outcomes, and to identify common methodological and reporting pitfalls. MEDLINE, EMBASE, Web of Science, and Scopus were searched from inception to February 5th, 2024 for AI studies predicting NMIBC recurrence or progression. APPRAISE-AI was used to assess methodological and reporting quality of these studies. Performance between AI and non-AI approaches included within these studies were compared. A total of 15 studies (five on recurrence, four on progression, and six on both) were included. All studies were retrospective, with a median follow-up of 71 months (IQR 32−93) and median cohort size of 125 (IQR 93−309). Most studies were low quality, with only one classified as high quality. While AI models generally outperformed non-AI approaches with respect to accuracy, c-index, sensitivity, and specificity, this margin of benefit varied with study quality (median absolute performance difference was 10 for low, 22 for moderate, and 4 for high quality studies). Common pitfalls included dataset limitations, heterogeneous outcome definitions, methodological flaws, suboptimal model evaluation, and reproducibility issues. Recommendations to address these challenges are proposed. These findings emphasise the need for collaborative efforts between urological and AI communities paired with rigorous methodologies to develop higher quality models, enabling AI to reach its potential in enhancing NMIBC care. [ABSTRACT FROM AUTHOR]

Decision-making capacity (DMC) among psychiatric inpatients is a pivotal clinical concern. A review by Okai et al. (2007) suggested that most psychiatric inpatients have DMC for treatment, and its assessment is reliable. Nevertheless, the high heterogeneity and mixed results from other studies mean there is considerable uncertainty around this topic. This study aimed to update Okai's research by conducting a systematic review with meta-analysis to address heterogeneity. We performed a systematic search across four databases, yielding 5351 results. We extracted data from 20 eligible studies on adult psychiatric inpatients, covering DMC assessments from 2006 to May 2022. A meta-analysis was conducted on 11 papers, and a quality assessment was performed. The study protocol was registered on PROSPERO (ID: CRD42022330074). The proportion of patients with DMC for treatment varied widely based on treatment setting, the specific decision and assessment methods. Reliable capacity assessment was feasible. The Mini-Mental State Examination (MMSE), Global Assessment of Function (GAF), and Brief Psychiatric Rating Scale (BPRS) predicted clinical judgments of capacity. Schizophrenia and bipolar mania were linked to the highest incapacity rates, while depression and anxiety symptoms were associated with better capacity and insight. Unemployment was the only sociodemographic factor correlated with incapacity. Assessing mental capacity is replicable, with most psychiatric inpatients able to make treatment decisions. However, this capacity varies with admission stage, formal status (involuntary or voluntary), and information provided. The severity of psychopathology is linked to mental capacity, though detailed psychopathological data are limited. [ABSTRACT FROM AUTHOR]

The present meta-analysis is an update of the meta-analysis by Schmucker and Lösel [Campbell Syst. Rev. 2017; 13: 1–75], which synthesized evidence on sexual recidivism as an indicator of treatment effectiveness in persons with sexual offense histories. The updated meta-analysis includes 37 samples comprising a total of 30,394 individuals with sexual offense histories, which is nearly three times the sample size reported by Schmucker and Lösel (2017: 28 samples, N = 9781). In line with Schmucker and Lösel (2017), the mean treatment effect was small with an odds ratio of 1.54 [95% CI 1.22, 1.95] (p <.001). A moderator analysis suggested three predictors of importance, i.e., risk level, treatment specialization, and author confounding. Greater treatment effectiveness was suggested in high- and medium-compared to low-risk individuals and in specialized compared to non-specialized treatments. Authors affiliated with treatment programs reported larger effectiveness than independent authors. These findings were overall in line with Schmucker and Lösel (2017), though the effects of risk level and treatment specialization were stronger in the current meta-analysis. The findings of the updated meta-analysis reinforce the evidence for the first and second principle of the Risk-Need-Responsivity model. The results may support researchers and decision-makers in interpreting the current evidence on sexual recidivism as an indicator of treatment effectiveness, and, based on that, implement and carry out informative, methodologically sound evaluations of ongoing treatment programs in persons with sexual offense histories. [ABSTRACT FROM AUTHOR]

Digital game-based learning (DGBL) interventions can be superior to traditional instruction methods for learning, but previous meta-analyses covered a huge period and included a variety of different target groups, limiting the results' transfer on specific target groups. Therefore, the aim of this meta-analysis is a theory-based examination of DGBL interventions' effects on different learning outcomes (cognitive, metacognitive, affective-motivational) in the school context, using studies published between 2015 and 2020 and meta-analytic techniques (including moderator analyses) to examine the effectiveness of DGBL interventions compared to traditional instruction methods. Results from random-effects models revealed a significant medium effect for overall learning (g =.54) and cognitive learning outcomes (g =.67). Also found were a small effect for affective-motivational learning outcomes (g =.32) and no significant effect for metacognitive learning outcomes. Additionally, there was no evidence of publication bias. Further meta-regression models did not reveal evidence of moderating personal, environmental, or confounding factors. The findings partially support the positive impact of DGBL interventions in school, and the study addresses its practical implications. [ABSTRACT FROM AUTHOR]

To address the societal harms of violence, many violence prevention interventions have been developed, tested, and implemented in the general population. These have been reported in systematic reviews and meta-analyses, which have typically focused on one type of intervention or outcome. We aimed to provide a comprehensive overview of the current evidence regarding the effectiveness of different psychosocial interventions in reducing all forms of violence toward others. We have conducted an umbrella review of previous meta-analyses using standard approaches and converted findings on effectiveness into odds ratios. We tested for the underlying quality of the meta-analytic evidence by examining heterogeneity, excess statistical significance, prediction intervals, and small study effects. We identified 16 meta-analyses, including nine investigating psychosocial interventions, and five legislative and policy changes. Most meta-analyses reported positive effects of tested interventions. The strongest effects were found for sports-based initiatives, and the weakest for general population programs aimed at early childhood, youth development, and reducing sexual assault perpetration by men. Legislative changes had varying effectiveness. We conclude that simple, scalable, and cost-efficient programs, such as sport-based initiatives, have the clearest empirical support as population-based approaches to violence prevention. [ABSTRACT FROM AUTHOR]

Given the pervasiveness of violent extremism all over the globe, understanding its psychological underpinnings is key in the fight against it. According to the Significance Quest Theory and its 3N model, violent extremism (i.e., violent and deviant behavior) is a function of three elements: need, narrative, and network. In the present meta-analysis, to put into test the theory and its model, we aimed to establish the strength of the association between these three elements, as well as the quest for significance itself, and violent extremism; and investigate if these associations are influenced by methodological decisions (i.e., sampling and measurements/manipulations). A literature search was performed through electronic platforms, a call for unpublished or in-press data, and backward snowballing. Seventeen reports, comprising 42 studies, met full inclusion criteria: quantitative studies based on primary data assessing for the association of at least one of the 3Ns, or quest for significance, and violent extremism, and providing sufficient data for effect size extraction. Findings are reported according to the Preferred Reporting Items for Systematic Reviews and Meta-Analyses(PRISMA) guidelines. Random-effect meta-analyses rendered statistically significant pooled effect sizes in all the investigated associations. The association is strong for quest for significance, moderate for narrative and network, and low for need for significance. Subgroup analyses demonstrate that the detection of these associations is influenced by methodological decisions concerning the measurements and manipulations, but not by those concerning the sampling. We discuss these findings and suggest future research directions aiming to improve the predictive power of the theory and its model. [ABSTRACT FROM AUTHOR]

Artificial intelligence (AI) has shown tremendous growth over the last decade, with the more recent development of clinical applications in health care. The ability of AI to synthesize large amounts of complex data automatically allows health care providers to access previously unavailable metrics and thus enhance and personalize patient care. These innovations include AI-assisted diagnostic tools, prediction models for each treatment pathway, and various tools for workflow optimization. The extension of AI into sports medicine is still early, but numerous AI-driven algorithms, devices, and research initiatives have delved into predicting and preventing athlete injury, aiding in injury assessment, optimizing recovery plans, monitoring rehabilitation progress, and predicting return to play. [ABSTRACT FROM AUTHOR]

Purpose: The purposes of this study were to: (a) meta-analyze the effects of academic press (AP) on K-12 student achievement in aggregate and in each examined learning subject; (b) meta-analyze the effect of school leadership of different leadership styles on AP; and (c) examine whether school level, subjects, and leadership or AP measures moderate these above-mentioned effects. Research Methods/Approach: Standard meta-analysis techniques were used to review 79 quantitative studies in the past 30 years and examine the multiple relationships between school leadership, AP, and student learning mentioned above. Heterogeneity analyses were conducted to identify moderators. Publication bias in these analysis results was also examined. Findings: AP had a large effect on student achievement; school leadership had a close to large effect on AP. These effects varied with leadership or AP measures and across school levels and student learning subjects. Implications for Research and Practice: The findings point to the importance of school leaders to improve student achievement by improving the level of AP in schools, especially high schools, and in schools with many economically disadvantaged students. Principals need to exercise a wide range of practices to push for academic excellence. [ABSTRACT FROM AUTHOR]

The present study was undertaken to understand the association of SNP 63(rs5030952) of the CAPN 10 gene with the condition. All statistical tests based on PRISMA and PROSPERO were performed on R studio (4.2.3). The pooled odds ratio (OR) estimates did not reveal any significant association between polymorphism at the SNP 63 locus of the CAPN 10 gene and T2DM under both the Common Effects Model (CEM) and Random Effects Model (REM). Further, subgroup analysis also did not reveal any significant OR implying that the SNP 63 polymorphism does not exhibit different effects in different ethnic groups. Therefore, it is concluded that polymorphism at the SNP63 locus of the CAPN 10 gene alone has no role to play in the etiology of T2DM. [ABSTRACT FROM AUTHOR]

Background: There is a growing body of evidence indicating a possible association between genetic variations and attention-deficit hyperactivity disorder (ADHD), although the results have been inconsistent. The objective of this study was to evaluate the correlation between the GRIN2A, GRIN2B and GRM7 gene polymorphisms and ADHD. Methods: A comprehensive meta-analysis and subgroup evaluation was conducted using a fixed-effects model to analyze the association between ADHD and GRIN2B (rs2284411), GRIN2A (rs2229193), and GRM7 (rs3792452) in six genetic models (dominant, recessive, overdominant, homozygous, heterozygous, and allele models). Results: The meta-analysis comprised 8 studies. The overall analysis showed that the GRIN2B rs2284411 T allele and T carries were significantly associated with a decreased risk of ADHD (dominant model:TT + CT vs. CC: OR = 0.783; 95% CI: 0.627-0.980; p = 0.032, allele model:T vs. C: OR = 0.795; 95% CI: 0.656-0.964; p = 0.019), especially in the Korean subgroup (dominant model:TT + CT vs. CC: OR = 0.640; 95% CI: 0.442-0.928; p = 0.019, overdominant model: CT vs. TT + CC: OR = 0.641; 95% CI: 0.438-0.938; p = 0.022, allele model:T vs. C: OR = 0.712; 95% CI: 0.521-0.974; p = 0.034 and heterozygous model: CT vs. CC: OR = 0.630; 95% CI: 0.429-0.925; p = 0.018). However, no meaningful associations were found for rs2229193 and rs3792452. [ABSTRACT FROM AUTHOR]

Currently, individual artificial intelligence (AI) algorithms face significant challenges in effectively diagnosing and predicting early stage emerging serious diseases. Our investigation indicates that these challenges primarily arise from insufficient clinical treatment data, leading to inadequate model training and substantial disparities among algorithm outcomes. Therefore, this study introduces an adaptive framework aimed at increasing prediction accuracy and mitigating instability by integrating various AI algorithms. In analyzing two cohorts of early cases of the coronavirus disease 2019 (COVID-19) in Wuhan, China, we demonstrate the reliability and precision of the adaptive combined learning algorithm. Employing an adaptive combination with three feature importance methods (Random Forest (RF), Scalable end-to-end Tree Boosting System (XGBoost), and Sparsity Oriented Importance Learning (SOIL)) for two cohorts, we identified 23 clinical features with significant impacts on COVID-19 outcomes. Subsequently, the adaptive combined prediction leveraged and enhanced the advantages of individual methods based on three forecasting algorithms (RF, XGBoost, and Logistic regression). The average accuracy for both cohorts exceeded 0.95, with the area under the receiver operating characteristics curve (AUC) values of 0.983 and 0.988, respectively. We established a severity grading system for COVID-19 based on the combined probability of death. Compared to the original classification, there was a significant decrease in the number of patients in the severe and critical levels, while the levels of mild and moderate showed a substantial increase. This severity grading system provides a more rational grading in clinical treatment. Clinicians can utilize this system for effective and reliable preliminary assessments and examinations of patients with emerging diseases, enabling timely and targeted treatment. [ABSTRACT FROM AUTHOR]

Purpose. Breast cancer is the most common cancer among women globally, with an incidence of approximately two million cases in 2018. Organised age-based breast cancer screening programs were established worldwide to detect breast cancer earlier and to reduce mortality. Currently, there is substantial anticipation regarding risk-adjusted screening programs, considering various risk factors in addition to age. The present study investigated the discriminatory accuracy of breast cancer risk prediction models and whether they suit risk-based screening programs. Methods. Following the PICO scheme, we conducted an overview of reviews and systematically searched four databases. All methodological steps, including the literature selection, data extraction and synthesis, and the quality appraisal were conducted following the 4-eyes principle. For the quality assessment, the AMSTAR 2 tool was used. Results. We included eight systematic reviews out of 833 hits based on the prespecified inclusion criteria. The eight systematic reviews comprised ninety-nine primary studies that were also considered for the data analysis. Three systematic reviews were assessed as having a high risk of bias, while the others were rated with a moderate or low risk of bias. Most identified breast cancer risk prediction models showed a low prognostic quality. Adding breast density and genetic information as risk factors only moderately improved the models' discriminatory accuracy. Conclusion. All breast cancer risk prediction models published to date show a limited ability to predict the individual breast cancer risk in women. Hence, it is too early to implement them in national breast cancer screening programs. Relevant randomised controlled trials about the benefit-harm ratio of risk-adjusted breast cancer screening programs compared to conventional age-based programs need to be awaited. [ABSTRACT FROM AUTHOR]

SARS-CoV-2 infection in children and young people (CYP) can lead to lifethreatening COVID-19, transmission within households and schools, and the development of long COVID. Using linked health and administrative data, we investigated vaccine uptake among 3,433,483 CYP aged 5–17 years across all UK nations between 4th August 2021 and 31st May 2022. We constructed national cohorts and undertook multi-state modelling and meta-analysis to identify associations between demographic variables and vaccine uptake. We found that uptake of the first COVID-19 vaccine among CYP was low across all four nations compared to other age groups and diminished with subsequent doses. Age and vaccination status of adults living in the same household were identified as important risk factors associated with vaccine uptake in CYP. For example, 5–11 year-olds were less likely to receive their first vaccine compared to 16–17 year-olds (adjusted Hazard Ratio [aHR]: 0.10 (95%CI: 0.06–0.19)), and CYP in unvaccinated households were less likely to receive their first vaccine compared to CYP in partially vaccinated households (aHR: 0.19, 95%CI 0.13–0.29). [ABSTRACT FROM AUTHOR]

The COVID-19 pandemic has had a significant impact on both the United Arab Emirates (UAE) and Malaysia, emphasizing the importance of developing accurate and reliable forecasting mechanisms to guide public health responses and policies. In this study, we compared several cutting-edge deep learning models, including Long Short-Term Memory (LSTM), bidirectional LSTM, Convolutional Neural Networks (CNN), hybrid CNN-LSTM, Multilayer Perceptron's, and Recurrent Neural Networks (RNN), to project COVID-19 cases in the aforementioned regions. These models were calibrated and evaluated using a comprehensive dataset that includes confirmed case counts, demographic data, and relevant socioeconomic factors. To enhance the performance of these models, Bayesian optimization techniques were employed. Subsequently, the models were re-evaluated to compare their effectiveness. Analytic approaches, both predictive and retrospective in nature, were used to interpret the data. Our primary objective was to determine the most effective model for predicting COVID-19 cases in the United Arab Emirates (UAE) and Malaysia. The findings indicate that the selected deep learning algorithms were proficient in forecasting COVID-19 cases, although their efficacy varied across different models. After a thorough evaluation, the model architectures most suitable for the specific conditions in the UAE and Malaysia were identified. Our study contributes significantly to the ongoing efforts to combat the COVID-19 pandemic, providing crucial insights into the application of sophisticated deep learning algorithms for the precise and timely forecasting of COVID-19 cases. These insights hold substantial value for shaping public health strategies, enabling authorities to develop targeted and evidence-based interventions to manage the virus spread and its impact on the populations of the UAE and Malaysia. The study confirms the usefulness of deep learning methodologies in efficiently processing complex datasets and generating reliable projections, a skill of great importance in healthcare and professional settings. [ABSTRACT FROM AUTHOR]

Background: Cardiogenic shock (CS) is the primary cause of death in patients suffering acute myocardial infarction. As an emerging and efficacious therapeutic approach, Chinese herbal injections (CHIs) are gaining significant popularity in China. However, the optimal CHIs for treating CS remain uncertain. Method: We searched eight databases from inception to 30 September 2023. Subsequently, we conducted the Bayesian network meta-analysis (NMA). Interventions were ranked based on the surface under the cumulative ranking curve (SUCRA) probability values. To compare the effects of CHIs on two distinct outcomes, a clustering analysis was performed. Furthermore, the quality of the studies was assessed. Results: For the study, we included 43 RCTs, encompassing 2,707 participants. The study evaluated six herbal injections, namely, Shenfu injection (SF), Shengmai injection (SM), Shenmai injection (Sm), Danshen injection (DS), Huangqi injection (HQ), and Xinmailong injection (XML). The analysis findings suggested that Sm (MD = -1.05, 95% CI: -2.10, -0.09) and SF (MD = -0.81, 95% CI: -1.40, -0.25) showed better efficacy compared to Western medicine (WM) alone in reducing in-hospital mortality. The SUCRA values revealed that Sm + WM ranked first in terms of in-hospital mortality, cardiac index (CI), and hourly urine output but second in improving left ventricular ejection fraction (LVEF) and mean arterial pressure (MAP). SF + WM, however, had the greatest impact on raising the clinical effective rate. In MAP, SM + WM came out on top. Moreover, in terms of safety, only 14 studies (31.8%), including five types of CHIs: SF, Sm, SM, HQ, and XML, observed adverse drug reactions. Conclusion: To summarize, this analysis discovered that, in terms of patients suffering from CS, CHIs + WM yielded significantly greater advantages than WM alone. Based on in-hospital mortality and the remaining outcomes, Sm performed excellently among all the involved CHIs. [ABSTRACT FROM AUTHOR]

Objective: Many studies have explored the relationship between childhood trauma and internet addiction from different theoretical perspectives; however, the results have been inconsistent. The purpose of this meta-analysis was to examine the relationship between childhood trauma and internet addiction. Methods: The PubMed, Web of Science, Embase, CNKI, Wanfang and VIP electronic databases were searched to identify studies examining the correlation between childhood trauma and adolescent internet addiction. The databases were searched from inception to December 31, 2022. Two researchers independently screened the literature, extracted the data, and evaluated the risk of bias of the included studies. Then, Stata 17.0 software was used to perform meta-analysis. Results: This study was registered on PROSPERO (CRD42023388699). A total of 19 studies involving 21,398 adolescents were included in this meta-analysis. The random effects model was used for pooled analysis, and the results revealed a strong positive association between childhood trauma and internet addiction (r 5 0.395, 95% CI [0.345, 0.442]). The relationship between childhood trauma and internet addiction was moderated by sample size, survey area, and internet addiction measurement tools. There were significant differences between the associations based on the various child trauma measurement tools and study quality scores. However, interstudy heterogeneity was not significantly affected by study year, sample source, or participant age. Conclusion: Internet addiction is positively correlated with childhood trauma. Therefore, it is extremely important for parents to provide a good growth environment during childhood to enhance the physical and mental development of adolescents. A warm family atmosphere helps individuals develop a healthy personality, thereby reducing or preventing the occurrence of internet addiction. Due to the limited number and low quality of the included studies, the above conclusions need to be verified by additional high-quality studies. [ABSTRACT FROM AUTHOR]

Background: Experiential learning is increasingly important in public health education. Purpose: Based on 33 studies, this is the first scoping review that provides clarity on the types of experiential learning activities (ELAs) used in public health education and the evaluation results from the perspectives of three stakeholders involved: students, faculty, and communities/external agencies. Methodology/Approach: A total of 1,912 citations were sourced from six electronic databases (Education Resources Information Centre, ProQuest Central, PubMed, Scopus, STM Source, and Web of Science) and manual searches (reference lists of included studies) independently by two researchers. A random-effects meta-analysis using forest plots and a meta-synthesis using a thematic synthesis approach were conducted. Findings/Conclusions: There was a diverse range of community-, classroom-, and work-focused ELAs used. Meta-analysis revealed positive outcomes in student satisfaction, problem-solving, and communication skills. This was triangulated with the meta-synthesis results, which also indicated an overwhelmingly positive attitude toward experiential learning by all three stakeholders. Implications: Future educational programs should consider the factors that contributed to the positive experiences from all three stakeholders' perspectives, address the various challenges highlighted in this review to refine the experience, and assess the negative experiences of faculty, and community/external agencies in greater detail. [ABSTRACT FROM AUTHOR]

Aims and Objectives: To synthesise and appraise the evidence of the efficacy of serious games in reducing chronic pain among older adults. Background: Chronic pain in older adults generally results in a substantial handicap due to decreased mobility, exercise avoidance and various concerns that affect their overall quality of life. While serious games have been widely used as a pain management approach, no reviews have thoroughly examined their efficacy for chronic pain management in older adult populations. Design: A systematic review and meta‐analysis. Methods: The CINAHL, the Cochrane Library, Embase, Medline, PubMed and Web of Science databases were comprehensively searched to find articles published from their inception until 17 April 2023. RoB‐2 was used to assess the risk of bias in the included studies. The efficacy of serious games for pain management in older individuals was investigated using pooled standardised mean differences (SMDs) in pain reduction using a random effect model. Results: The meta‐analysis comprised nine randomised controlled trials that included 350 older adult patients with pain. Serious games effectively alleviated pain in this group (pooled SMD = −0.62; 95% confidence interval: −1.15 to −0.10), although pain‐related disability and fear require further investigation. Conclusions: Serious games tended to effectively reduce pain in this older adult group; however, due to a lack of randomised controlled trials, the analysis found lower effectiveness in reducing pain‐related disability and fear. Further studies are accordingly required to confirm these findings. Relevance to Clinical Practice: The findings of the study emphasise the importance of serious games to increase the motivation of older adults to exercise as one of the safe and extensively used pain management strategies. Serious games that effectively reduce chronic pain in older adults are characterised as consisting of diverse physical activities delivered through consoles, computer‐based activities and other technologies. Patient or Public Contributions: Serious games are recommended as being potentially useful and practical for reducing pain in older adults. [ABSTRACT FROM AUTHOR]

While it is common to monitor deployed clinical artificial intelligence (AI) models for performance degradation, it is less common for the input data to be monitored for data drift – systemic changes to input distributions. However, when real-time evaluation may not be practical (eg., labeling costs) or when gold-labels are automatically generated, we argue that tracking data drift becomes a vital addition for AI deployments. In this work, we perform empirical experiments on real-world medical imaging to evaluate three data drift detection methods’ ability to detect data drift caused (a) naturally (emergence of COVID-19 in X-rays) and (b) synthetically. We find that monitoring performance alone is not a good proxy for detecting data drift and that drift-detection heavily depends on sample size and patient features. Our work discusses the need and utility of data drift detection in various scenarios and highlights gaps in knowledge for the practical application of existing methods. [ABSTRACT FROM AUTHOR]

Time reference is used to build the temporal framework of discourse and is essential in ensuring efficient communication. Several studies have reported time reference deficits in fluent and non-fluent aphasia and have shown that tenses (past, present, future) are not all impaired to the same extent. However, there is little consensus on the dissociations between tenses, and the question of the influence of the type of aphasia (fluent vs. non-fluent) on time reference remains open. Therefore, a systematic review and an individual participant data meta-analysis (or mega-analysis) were conducted to determine (1) whether one tense is more impaired than another in fluent and non-fluent aphasia and, if so, (2) which task and speaker-related factors moderate tense effects. The systematic review resulted in 35 studies reporting the performance in time reference of 392 participants. The mega-analysis was then performed on 23 studies for a total of 232 participants and showed an alteration of past tense compared to present and future tenses in both types of aphasia. The analysis also showed a task and an age effect on time reference but no gender effect, independently of tenses. These results add to our knowledge of time reference in aphasia and have implications for future therapies. [ABSTRACT FROM AUTHOR]

Objective. To understand how congenital toxoplasmosis (CT) diagnosis has evolved over the years, we performed a systematic review and meta-analysis to summarize the kind of analysis that has been employed for CT diagnosis. Methods. PubMed and Lilacs databases were used in order to access the kind of analysis that has been employed for CT diagnosis in several samples. Our search combined the following combining terms: "congenital toxoplasmosis" or "gestational toxoplasmosis" and "diagnosis" and "blood," "serum," "amniotic fluid," "placenta," or "colostrum." We extracted data on true positive, true negative, false positive, and false negative to generate pooled sensitivity, specificity, and diagnostic odds ratio (DOR). Random-effects models using MetaDTA were used for analysis. Results. Sixty-five articles were included in the study aiming for comparisons (75.4%), diagnosis performance (52.3%), diagnosis improvement (32.3%), or to distinguish acute/chronic infection phases (36.9%). Amniotic fluid (AF) and placenta were used in 36.9% and 10.8% of articles, respectively, targeting parasites and/or T. gondii DNA. Blood was used in 86% of articles for enzymatic assays. Colostrum was used in one article to search for antibodies. In meta-analysis, PCR in AF showed the best performance for CT diagnosis based on the highest summary sensitivity (85.1%) and specificity (99.7%) added to lower magnitude heterogeneity. Conclusion. Most of the assays being researched to diagnose CT are basically the same traditional approaches available for clinical purposes. The range in diagnostic performance and the challenges imposed by CT diagnosis indicate the need to better explore pregnancy samples in search of new possibilities for diagnostic tools. Exploring immunological markers and using bioinformatics tools and T. gondii recombinant antigens should address the research needed for a new generation of diagnostic tools to face these challenges. [ABSTRACT FROM AUTHOR]

Debate is ongoing on the efficacy of cognitive behavior therapy (CBT) for myalgic encephalomyelitis or chronic fatigue syndrome (ME/CFS). With an individual patient data (IPD) meta-analysis we investigated whether the effect of CBT varied by patient characteristics. These included post-exertional malaise (PEM), a central feature of ME/CFS according to many. We searched for randomized controlled trials similar with respect to comparison condition, outcomes and treatment-protocol. Moderation on fatigue severity (Checklist Individual Strength, subscale fatigue severity), functional impairment (Sickness Impact Profile-8) and physical functioning (Short Form-36, subscale physical functioning) was investigated using linear mixed model analyses and interaction tests. PROSPERO (CRD42022358245). Data from eight trials (n = 1298 patients) were pooled. CBT showed beneficial effects on fatigue severity (β = −11.46, 95% CI −15.13 to −7.79); p < 0.001, functional impairment (β = −448.40, 95% CI −625.58 to −271.23); p < 0.001; and physical functioning (β = 9.64, 95% CI 3.30 to 15.98); p < 0.001. The effect of CBT on fatigue severity varied by age (p interaction = 0.003), functional impairment (p interaction = 0.045) and physical activity pattern (p interaction = 0.027). Patients who were younger, reported less functional impairments and had a fluctuating activity pattern benefitted more. The effect on physical functioning varied by self-efficacy (p interaction = 0.025), with patients with higher self-efficacy benefitting most. No other moderators were found. It can be concluded from this study that CBT for ME/CFS can lead to significant reductions of fatigue, functional impairment, and physical limitations. There is no indication patients meeting different case definitions or reporting additional symptoms benefit less from CBT. Our findings do not support recent guidelines in which evidence from studies not mandating PEM was downgraded. [ABSTRACT FROM AUTHOR]