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PubMed2026

Advancing nursing education through social and emotional learning: A systematic review guided by the Collaborative for Academic, Social, and Emotional Learning framework.

BACKGROUND: With Generation Z entering the nursing workforce in growing numbers, strengthening social and emotional learning is critical for academic success, professional adaptation, and safe practice. However, the existing evidence remains fragmented because of varied interventions and inconsistent approaches. OBJECTIVES: This systematic review examined (1) the social and emotional learning essential for nursing students and nurses within the Collaborative for Academic, Social, and Emotional Learning framework, (2) their impact on educational and clinical outcomes, and (3) implications for advancing nursing education and practice. METHODS: Following Joanna Briggs Institute methodology and Preferred Reporting Items for Systematic Reviews and Meta-Analyses guidelines, five international (PubMed, EMBASE, CINAHL, PsycINFO, Cochrane) and three Korean (RISS, KoreaMed, KMBASE) databases were searched up to June 2025. Eighteen studies involving 2,952 participants met the inclusion criteria, including quasi-experimental quantitative studies, descriptive quantitative studies, qualitative studies, and mixed-methods studies. The methodological quality of the included studies was appraised using the Mixed Methods Appraisal Tool. RESULTS: Within the Collaborative for Academic, Social, and Emotional Learning framework, relationship skills and self-management were the most frequently studied competencies, emphasizing teamwork, communication, and stress regulation. Self-awareness and social awareness were underexplored, despite their importance in empathy, resilience, and reflective practice. Responsible decision-making was the least studied competency, despite its importance in ethical reasoning. Social and emotional learning was consistently associated with enhanced adaptation, communication, leadership, relationships, and clinical performance. Effective strategies included blended learning, simulation, reflective activities, and mentorship, which are aligned with Generation Z's learning preferences. CONCLUSION: Although social and emotional learning integration is associated with improvements in educational and clinical outcomes in nursing, current research has largely centered on relational and stress-related competencies while underrepresenting responsible decision-making. To cultivate reflective, empathetic, and ethically grounded nurses, curricula should integrate social and emotional learning through a balanced and structured approach. REGISTRATION: This study was registered on PROSPERO (ID: CRD420251005683).

Humans↗

PubMed2026

Individual differences in brain dynamics across a social cognition network induced by cortico-cerebellar tDCS in adults with autism spectrum disorder (ASD).

Autism spectrum disorder (ASD) is a neurodevelopmental condition with core diagnostic domains of social communication impairments, restricted interests and repetitive behaviors. Idiosyncratic brain organization is a potential hallmark of ASD. Previous transcranial direct current stimulation (tDCS) studies often targeted dorsolateral prefrontal cortex, with changes oin brain dynamics averaged across the cohort. We utilized a magnetoencephalographic (MEG) array to characterize individual differences in brain dynamics induced by cortico-cerebellar tDCS across nodes of a social cognition network. A randomized, sham-controlled, double-blind, within-subject clinical trial was conducted in a cohort of 24 young adults with ASD or high autistic traits. Two separate sessions of computerized social learning activities were combined with verum/sham tDCS, with anodal electrode over right temporoparietal junction (TPJ) and cathode on right deltoid. Following stimulation, theta- and alpha-band activity were evaluated within nodes of a social cognition network: bilateral TPJ, fusiform, medial prefrontal cortex and Crus I/II of cerebellum. Idiosyncratic participant-specific up- and down-regulation of theta- and alpha-band activity occurred across the network. Activity in right Crus I/II, a region inundated by the stimulation current, strongly correlated with the change of activity summed across all cerebral cortical nodes in theta- but not alpha-band. Intrinsic theta-band activity is believed to mediate input/output relationships in cerebellar cortex and to drive synaptic plasticity. These results suggest that theta-band stimulation of cerebellar cortex might be an effective therapy for individuals on the autism spectrum who present with cerebellar hyperactivity.

Humans↗

PubMed2026

Identifying stakeholder behaviors for competency-based pharmacy education: A stage 1 behavior change wheel analysis.

INTRODUCTION/OBJECTIVES: Competency-Based Pharmacy Education (CBPE) is a strategic priority for preparing graduates to meet evolving healthcare needs. However, efforts to implement CBPE can stall due to behavioral challenges among faculty, administrators, preceptors, and learners. This study aimed to apply Stage 1 of the Behavior Change Wheel (BCW) to identify stakeholder-specific behaviors and associated determinants needed to implement the five core components of CBPE. METHODS: A multi-method approach grounded in the BCW, the Capability, Opportunity, Motivation - Behavior (COM-B) model, and the Theoretical Domains Framework (TDF) was used. Data were gathered through (1) targeted literature review; (2) structured focus groups with competency-based education experts and pharmacy education stakeholders; and (3) an iterative consensus process. Behaviors were mapped to the five CBPE components: (1) defined competencies, (2) developmental progression, (3) tailored instruction, (4) authentic experiential learning, and (5) programmatic assessment, and then mapped to COM-B and TDF constructs. RESULTS: Over fifty stakeholder-specific behaviors were identified and specified across the CBPE framework. This revealed shared barriers such as limited instructional design knowledge (psychological capability), insufficient assessment of infrastructure (physical opportunity), and misaligned professional identity (reflective motivation). Key TDF domains included knowledge, environmental context, beliefs about capabilities, and professional roles. The behavioral problem statements, specifications, and determinants were identified to support future intervention planning. CONCLUSION: This Stage 1 analysis provides a behaviorally grounded foundation for CBPE implementation by identifying stakeholder behaviors and conditions that enable change. These findings will inform the development of readiness-to-change assessments and targeted interventions (BCW Stages 2 and 3), supporting scalable and sustainable CBPE transformation in pharmacy education.

Education, Pharmacy↗

PubMed2026

Association between cumulative social disadvantage, as measured by the social determinants of health score, and epilepsy: a cross-sectional study.

BACKGROUND: Social determinants of health (SDoH) shape access to care, health behaviors, and long-term outcomes, yet their cumulative relationship with epilepsy has not been well quantified. This study examined whether a composite SDoH score was associated with epilepsy in adults. METHODS: This cross-sectional study used data from the National Health and Nutrition Examination Survey 2013-2018. The SDoH score ranged from 0 to 8 and summarized eight unfavorable social conditions. Epilepsy was identified using medication-based ascertainment. Survey-weighted logistic regression models were applied to evaluate the association between SDoH score and epilepsy. Restricted cubic spline, subgroup, sensitivity, and receiver operating characteristic analyses were also performed. RESULTS: A total of 13,119 participants were included, of whom 114 had epilepsy. Participants with epilepsy had a higher mean SDoH score than those without epilepsy (3.41 ± 0.24 vs. 2.35 ± 0.06, P < 0.001). In the fully adjusted model, each 1-point increase in SDoH score was associated with 31% higher odds of epilepsy (OR 1.31, 95% CI 1.16-1.48). Compared with the low-score group (0-2), the adjusted odds ratios were 2.09 (95% CI 1.06-4.15) for scores of 3-5 and 2.67 (95% CI 1.34-5.33) for scores of 6-8. Spline analysis showed a significant overall association without evidence of nonlinearity. Adding SDoH components to demographic variables improved model discrimination (AUC 0.731 vs. 0.589, P for difference <0.001). CONCLUSION: Greater cumulative social disadvantage, as reflected by the SDoH score, was associated with higher odds of epilepsy.

Humans↗

PubMed2026

Effects of acute inflammatory challenge on anhedonia and social reward processing: A randomized controlled trial of endotoxin.

Anhedonia is a core feature of depression that reflects reward system dysfunction. Acute inflammatory challenge has been shown to induce transient anhedonia and reward processing deficits, but its effects on distinct components of social reward processes remain poorly understood. This randomized, double-blind, placebo-controlled study evaluated anhedonia and social reward responsivity following acute inflammatory challenge. Low-dose endotoxin (0.8 ng/kg body weight; n = 18) or placebo (same volume of 0.9% saline; n = 22) was administered to female adults (ages 25-44). Anhedonia (Snaith-Hamilton Pleasure Questionnaire) and blood samples for cytokine assessment were collected pre-infusion and 2 and 4 h post-infusion. Social reward motivation (wanting to engage in a social task) was assessed at a baseline eligibility visit and 2 h post-infusion. Sensitivity to positive social cues (attentional bias toward and recognition of happy faces) was assessed pre-infusion and 2 h post-infusion. Results showed that endotoxin increased anhedonic symptoms (p = .005) and decreased social motivation by 2 h post-infusion (p = .001); within the endotoxin condition, changes in both outcomes were associated with increases in TNF-α (p's < .04). A significant condition-by-time interaction for happy-face recognition (p = .011) was driven by increased accuracy in the placebo (p = .04) but not endotoxin (p = .43) condition. Endotoxin administration did not affect attentional bias toward happy faces. Findings suggest that acute inflammatory challenge induces anhedonia and reduces social motivation, with more limited effects on sensitivity to positive social cues, highlighting specific reward-related pathways through which immune activation may contribute to inflammation-associated depression. Future work could examine whether immune-related changes in social motivation contribute to motivational deficits in depression.

Humans↗

PubMed2026

Effectiveness of Embedded Social Media Content on E-cigarette Attitudes and Behaviors: Results from a Randomized Control Trial.

BACKGROUND: This longitudinal randomized controlled trial examined the effects of anti-vaping social media content on e-cigarette attitudes and intentions among U.S. young adults (ages 18-24; n=3,400). METHODS: Targeted content was embedded directly into participants' social media feeds, with varying levels of impressions. RESULTS: Results indicate that increased exposure to anti-vaping messages significantly elevated perceived risk of harm (β=0.11, 95% CI: 0.03-0.20, p < .01) and social unacceptability of e-cigarette use (β=0.10, 95% CI: 0.03-0.18, p < .01), while decreasing intentions to vape (RRR = 0.59, 95% CI: 0.36-0.97, p < .05). Notably, these attitudinal shifts occurred even with relatively low ad exposure and over an extended intervention period, while controlling for the e-cigarette use status (never, former, or current) of participants. Discussion These findings support the effectiveness of digital media campaigns in influencing health-related behaviors and attitudes among young adults. Specifically, embedding anti e-cigarette content directly in the feed of young adults is shown to be effective in shifting attitudes in a space where these young adults are already engaged. Limitations include limited ad impressions and minimal change in ad awareness recall, suggesting future research should explore longer interventions and broader nicotine product messaging.

Humans↗

PubMed2026

Does future-oriented imagery rescripting increase willingness to carry out a social anxiety-related behavioral experiment? An extended replication.

BACKGROUND AND OBJECTIVES: Mental images of threat are common in social anxiety disorder and could impede exposure to fear-relevant situations. Landkroon et al. (2022) found that imagery rescripting of anticipated future threat, compared to no-task, increases willingness of non-clinical individuals to conduct a social anxiety-related behavioral experiment. The aim of this two-day preregistered extended replication study was to examine whether the effects persist beyond the intervention session by scheduling the behavioral experiment 1-7 days after the intervention. METHODS: On Day 1, 60 pre-screened participants were asked to design a behavioral experiment to test an idiosyncratic negative belief about a feared social situation. They were then randomly assigned to imagery rescripting (focused on their worst feared outcome of the behavioral experiment) or no-task. Participants' willingness to do the behavioral experiment and their threat beliefs was measured. On Day 2, they were asked to carry out the behavioral experiment. RESULTS: The imagery rescripting group, compared to the control group, did not show increased willingness or compliance to do the behavioral experiment on Day 1 or 2. Furthermore, the imagery rescripting did not lead to greater reductions in threat beliefs on either day. LIMITATIONS: The quality of the imagery rescripting intervention was not assessed, and given the brief nature of the intervention, any effects may have dissipated before participants decided whether to carry out the behavioral experiment. CONCLUSIONS: There was no evidence that imagery rescripting leads to changes in willingness or compliance to do a fear-relevant behavioral experiment. More research is needed to examine interventions that can enhance exposure in socially anxious people.

Humans↗

PubMed2026

Post-weaning social isolation increases reward-seeking behavior in mice.

Social isolation is a growing public health concern. Although isolation at any age is harmful, previous studies have shown that isolation during adolescence, correlating with critical periods of brain development, can impair cognitive function and increase the risk for psychiatric illness later in life. In this study, we utilized a mouse model of social isolation (SI) during adolescence (postnatal day 21-35) and compared performance of isolated and group-housed mice on a touchscreen-based continuous performance test (rCPT) and fixed ratio/progressive ratio (FR/PR) tasks in adulthood. SI improved performance in the rCPT and the improvement in performance was consistent across time bins within the 45-minute testing session. There were no effects of SI on reaction times or reward retrieval latencies. A possible confound for performance in the rCPT would be SI-induced changes in reward-seeking or motivation for the strawberry milk reward. We next compared the SI mice to their group-housed littermate controls on both PR and FR schedules of reinforcement and found that SI mice had higher breakpoints on a PR4 schedule and earned significantly more reinforcers on an FR1 schedule of reinforcement compared to their group-housed littermates, suggesting that high performance in the CPT may be due to increased motivation for food rewards. These data indicate that SI during adolescence has significant effects on reward-seeking behavior in adult mice and may provide a useful behavioral model for studying the link between SI and risk for neuropsychiatric disorders.

Animals↗

PubMed2026

Affective reactivity to a remote computer-based Trier Social Stress Test during a planned quit attempt: associations with short-term cigarette smoking lapse risk.

BACKGROUND: The Trier Social Stress Test (TSST) elicits affective responses and has been linked to smoking behavior. However, its remote use during a planned quit attempt-when stress reactivity may influence early lapse-remains understudied. OBJECTIVE: To quantify affective reactivity to a remotely administered TSST on a planned quit date following overnight abstinence and evaluate associations with cigarette use and lapse within 48 h. METHODS: This secondary analysis used data from a randomized controlled trial of adult smokers completing a remotely administered TSST following overnight nicotine abstinence. Urge, anxiety, and stress were assessed using visual analog scales and summarized using area under the curve (AUC) metrics. Smoking outcomes included cigarette count and lapse within 48 h. Associations were estimated using generalized estimating equations. RESULTS: In adjusted models, anxiety reactivity-but not urge or stress-was associated with cigarette count and lapse. Greater anxiety exposure (AUCtot) and change above baseline (AUCab) were associated with higher cigarette count (IRR=1.0004, 95%CI:1.0002-1.001, p=.002; IRR=1.01, 95%CI: 1.002-1.01, p=.002) and increased odds of lapse (OR=1.001, 95%CI: 1.0001-1.002, p=.03; OR=1.02, 95%CI: 1.001-1.03, p=.03). Effect sizes were small. CONCLUSIONS: Anxiety reactivity under nicotine deprivation was associated with increased cigarette use and lapse 48 h post quit attempt, suggesting individual differences in stress-evoked anxiety may serve as a behavioral marker for early lapse. Remote TSST administration appears feasible for eliciting affective responses on a quit date.

Humans↗

PubMed2026

Angiotensin II regulates anxiety and social-affective top-down and bottom-up attention control in a sex-dependent manner.

BACKGROUND: The renin-angiotensin system (RAS) has been increasingly recognized as potent modulator of cognitive and affective functions, with angiotensin II type 1 receptor (AT1R) antagonists emerging as repurposing candidate for anxiety and stress-related disorders. However, it remains unclear whether transient AT1R blockade modulates emotional attentional control and whether these effects are sex-dependent. METHODS: We conducted a preregistered, randomized, double-blind, placebo-controlled pharmacological eye-tracking study in 79 healthy adults (males and females) and determined effects of transient AT1R blockade via losartan (50 mg) on emotional attention control using a validated anti-saccade paradigm with social (emotional faces) and non-social stimuli. Treatment effects on state anxiety and oculomotor responses were characterized using traditional metrics and a novel trial-history informed dynamic control framework. RESULTS: Losartan reduced state anxiety irrespective of sex but induced sexually dimorphic effects on attentional control. In females, losartan enhanced performance by reducing endpoint error without altering latency. Conversely, in males, losartan increased endpoint error and prolonged latency of the first correct saccade. Trial-history analyses revealed losartan reduced error probabilities following errors and repeat trials in both sexes. Yet, following correct trials, females receiving losartan maintained lower error probabilities, while males exhibited higher errors, potentially reflecting failure to disengage from effortful control. CONCLUSIONS: The RAS modulates anxiety and attentional control, the latter sex-dependently. AT1R blockade reconfigures attentional processing and adaptive control, suggesting sex-specific therapeutic potential in disorders characterized by excessive anxiety and attentional dysregulation. CLINICAL TRIALS REGISTRATION: ClinicalTrials.gov; https://clinicaltrials.gov/;NCT06329050.

Humans↗

PubMed2026

Effect of cognitive enhancement therapy for early course schizophrenia on gray matter volume: A confirmatory multisite randomized clinical trial.

BACKGROUND: Cognitive Enhancement Therapy (CET) is an evidence-based cognitive remediation intervention for early course schizophrenia with established benefits for cognition. CET may protect against broad temporolimbic gray matter volume loss associated with cognitive improvement, but this finding has yet to be replicated. This research reexamined if CET protects against temporolimbic gray matter volume loss in an independent and larger multisite early course sample, and if this neuroprotective effect predicts cognitive and social adjustment improvement. METHODS: Ninety-nine participants with early course schizophrenia completed MRI, cognitive, and social adjustment assessments at baseline, 9 (mid-treatment), and 18 (end of treatment) months. Linear mixed-effects models examined the differential impact of CET (n = 56) compared to Enriched Supportive Therapy (n = 43) on temporolimbic gray matter volume in regions-of-interest (ROIs) that previously demonstrated CET-related neuroprotection (primary ROIs), as well as frontotemporal ROIs outlined in the first trial (secondary ROIs). RESULTS: The right rostral anterior cingulate was the only primary ROI to demonstrate a significant group × time interaction, but was unrelated to cognitive and social adjustment change. No secondary ROIs exhibited a differential treatment effect. CONCLUSION: This confirmatory trial did not recapitulate the observed broad pattern of CET-related temporolimbic gray matter volume neuroprotection. Consistent with the larger literature demonstrating limited evidence of cognitive remediation effects on the brain in schizophrenia, these findings underscore the need for continued investigation of CET-related changes with other neuroimaging modalities, especially given its established cognitive benefits. Such information is critical for cognitive remediation optimization based on validated therapeutic mechanisms.

Humans↗

PubMed2026

Analysis of deep learning techniques in computer-aided diagnosis for meniscus injuries: a systematic literature review.

Meniscus informatics is a growing subject of study in the healthcare industry. One of the major hindrances to the healthcare system's transformation is obtaining knowledge and meaningful information from complicated, high-dimensional and diverse sources. Modern biomedical research, for instance, has seen an increase in the use of complex, dissimilar, poorly documented, and generally unstructured electronic health records, imaging, sensor data and text, even after many current techniques have been used to extract more robust and useful elements from the data for analysis. New efficient standards for building end-to-end learning models from complex data are therefore needed. Therefore, the current study aims to examine the most recent research on the use of deep learning techniques for diagnosing meniscus tears and recommend creating comprehensive and meaningful interpretable structures that might benefit the healthcare industry. We also draw attention to shortcomings and the need for better technique development, and we provide new perspectives about this exciting new development in the field.

Humans↗

PubMed2026

Development, feasibility, acceptability, and preliminary impact of NutriSOS®: A behavioral mobile app to promote sustainable diets.

The primary objective of this study was to describe the development of the NutriSOS® app and to evaluate its feasibility and acceptability for its use in the NutriSOS® Randomized Controlled Trial (RCT) to promote sustainable diets. A secondary objective was to explore preliminary changes in dietary and physical activity behaviors and environmental impact following app use. The NutriSOS® app integrates personalized dietary advice, educational content, self-monitoring, and social interaction features. A single-arm, pre-post pilot study was conducted in 37 young Mexican adults over four weeks. Feasibility, acceptability, quality, and usability were assessed using online surveys, alongside exploratory changes in dietary and physical activity behaviors, environmental indicators, and their association with perceived behavioral determinants. Feasibility and acceptability were high overall, with favorable responses reaching up to 100% in key components such as the nutritional guide and learning modules, and above 90% for messaging, registration, and design. Greater variability was observed in some sections, particularly the 24-h recall (41-86%). Reductions in red and processed meat and ultra-processed food consumption were observed (from 3 to 1 times/week, p < 0.01), with ∼60% decreases in their related environmental footprints (p < 0.01) and favorable self-reported behavioral determinants (p < 0.0001). Physical activity type and intensity changed (p < 0.05). These findings support NutriSOS® as a feasible and acceptable tool, while highlighting areas for refinement, particularly those related to the time and effort required for data entry, prior to its implementation in the NutriSOS® RCT, in which its effectiveness will be formally evaluated.

Humans↗

PubMed2026

A systematic review of human avoidance learning: Cognition, computation, and methods.

Avoidance behaviour is fundamental for survival but can become maladaptive in clinical conditions. A large body of literature has accumulated on the dynamics of human avoidance learning. However, current theories and overviews do not provide an exhaustive account of this evidence. In this systematic review, we identify N = 116 studies on human avoidance learning. We analyse these studies with the goal of distilling robust empirical phenomena as a basis for theory-building, and examine their diagnostic value in differentiating between competing theories. We find that the evidence is difficult to reconcile with foundational two-factor and classical safety-signal accounts, and most strongly supports expectancy- and inference-based views, in which avoidance responses are selected with respect to represented consequences. At the same time, no current framework provides a complete account of the evidence: several findings point to an additional role for operant valuation, Pavlovian influences, and contextual or latent-state control over the expression of avoidance. Methodologically, we observe that the problem setting in the most common experimental paradigms is radically simpler than real-world avoidance and therefore unlikely to expose the limits of inferential or reflective mechanisms. Consequently, we argue that paradigms with greater computational demands and more realistic action affordances are required to identify the mechanisms underlying avoidance learning. Collectively, these insights provide a foundation for theoretical refinement, computational modelling, and methodological innovation, with implications for advancing interventions targeting maladaptive avoidance.

Humans↗

PubMed2026

Machine learning-based prediction of unplanned readmission and construction of an online calculator for elderly patients with mild ischemic stroke.

OBJECTIVE: To screen for independent risk factors for unplanned readmission in elderly patients with mild ischemic stroke, and to construct and validate an online risk prediction calculator based on an interpretable machine learning model, thereby providing a promising practical tool for accurate clinical assessment of 30‑day all‑cause unplanned readmission risk in this population. METHODS: A prospective cohort study was conducted, including 1050 patients aged ≥ 60 years with mild ischemic stroke admitted between August 2023 and September 2024. Participants were randomly divided into a training set (840 cases) and a test set (210 cases) at a ratio of 8:2. Risk factors were screened by univariate analysis and multivariable Logistic regression. Four machine learning models, namely LightGBM, XGBoost, Random Forest, and K‑Nearest Neighbors (KNN), were developed and their performance was evaluated using AUC, accuracy, sensitivity, and specificity as metrics. The SHAP framework was used for interpretability analysis, and an online calculator was subsequently developed based on the optimal model. RESULTS: Univariate analysis showed significant differences (P < 0.05) in 13 factors including age, smoking, AIP, TyG index, HALP score, etc. Multivariable Logistic regression identified age (OR = 9.752), smoking (OR = 5.171), AIP (OR = 6.691), TyG index (OR = 4.393), HALP score (OR = 2.831), and ≥ 2 comorbidities (OR = 3.664) as independent risk factors. All four machine learning models demonstrated good predictive performance. Based on a comprehensive evaluation of multiple metrics and computational efficiency, the LightGBM model exhibited the best predictive performance (AUC = 0.884, accuracy = 0.829, sensitivity = 0.812, specificity = 0.875). SHAP analysis showed that age, AIP, TyG index, smoking, and HALP score were key predictors. An online calculator developed based on this model enables individualized risk predictions. CONCLUSION: Key risk factors associated with 30‑day unplanned readmission in elderly patients with mild ischemic stroke were identified. The LightGBM model demonstrated high predictive accuracy, and together with the interpretability analysis and online calculator, offers a practical tool to support clinical risk assessment. However, this tool requires future external validation.

Humans↗

PubMed2026

Diagnostic performance of machine learning models for malignant and non-malignant pleural effusion: Systematic review and meta-analysis.

BACKGROUND: Accurately distinguishing malignant pleural effusion (MPE) from non-malignant pleural effusion is clinically important, but the generalisability and methodological quality of machine-learning (ML) models remain uncertain. METHODS: We searched eight databases to 23 April 2026. Diagnostic performance was pooled using random-effects and Reitsma bivariate models, and study quality was assessed using PROBAST+AI. RESULTS: Forty-two studies were included; 17 contributed to the AUC meta-analysis and 14 to the bivariate analysis. The pooled AUC was 0.90 (95 % CI 0.85-0.94; 95 % prediction interval 0.62-0.98), with sensitivity of 0.80 (95 % CI 0.77-0.83) and specificity of 0.87 (95 % CI 0.79-0.92). Only nine studies reported external, temporal or independent validation. Externally validated studies had a lower pooled AUC than studies without external validation (0.83 vs 0.92), with lower specificity observed in the two externally validated studies contributing sensitivity and specificity data. All 42 development assessments had high overall quality concerns, and all 42 model evaluations were judged at high risk of bias. CONCLUSIONS: ML models showed good apparent accuracy for distinguishing MPE from non-MPE, but the evidence was limited by substantial heterogeneity, high risk of bias and scarce external validation. The pooled estimates reflect the average performance of different selected models rather than the expected accuracy of a single clinical test. ML models should be regarded as adjuncts to existing diagnostic pathways until they are confirmed by rigorous multicentre prospective external validation and clinical-impact studies.

Humans↗

PubMed2026

Effects of extended problem-based learning interventions on undergraduate nursing education: A systematic review.

OBJECTIVE: Exploring the effects of long-term PBL (problem-based learning) intervention on undergraduate nursing students. METHODS: The article retrieved literature from CINAHL Complete, Academic Search Complete, Web of Science, PubMed, EMBASE, OVID, and Cochrane Library up to January 2025. Studies had to meet all of these criteria: (1) They used a randomized controlled trial (RCT) and quasi-experimental design. (2) The PBL pedagogy intervention lasted 4 weeks or longer. (3) The participants were undergraduate nursing students. (4) They reported primary outcomes. These included critical thinking, problem-solving skills and self-directed learning. Two researchers screened articles, extracted data, and assessed quality independently using blinding. They used Cochrane ROB2 for RCTs and ROBINS-I for quasi-experimental studies to judge bias risk. Meta-analysis was performed using RevMan 5.4 software. For continuous variables, standardized mean difference (SMD) and 95% confidence interval were calculated. Heterogeneity was assessed by I2 statistic. When I2 > 50%, sensitivity analysis was conducted. The source of heterogeneity was explored by excluding studies one by one. The primary outcomes included standardized critical thinking, problem-solving, and self-directed learning assessment results. RESULTS: A total of 11 randomized controlled trials and quasi-experimental studies were retrieved and included for meta-analysis. The experimental group significantly outperformed the control group in critical thinking, problem-solving, and self-directed learning, with differences being statistically significant (P ≤ 0.05). However, high heterogeneity was observed. After sensitivity analysis, the heterogeneity was reduced and the results remained statistically significant, indicating that the findings were not solely dependent on the excluded studies.

Problem-Based Learning↗

PubMed2026

Comparative effectiveness of game-based learning modalities in nursing and medical education: a systematic review and Bayesian network meta-analysis.

BACKGROUND: Game-based learning (GBL) is increasingly used in healthcare education, but educators must choose among diverse modalities (e.g., quiz platforms, apps, serious games and metaverse environments). Comparative evidence on which modalities perform best across learning domains (knowledge, attitudes, and practice) remains limited. AIM: To compare the effects of distinct GBL modalities on knowledge, attitudes, and practice outcomes in nursing and medical education and to explore whether comparative effects differ by learner group (pre-licensure students and in-service professionals). DESIGN: PRISMA-NMA-aligned systematic review and Bayesian network meta-analysis. METHODS: We searched eight databases and trial registries through September 2, 2024, for randomized controlled trials comparing GBL with traditional teaching (TT). Outcomes were transformed to a 0-100 scale and analysed as change from baseline in Bayesian consistency models; random-effects models were selected using deviance information criterion (DIC). Risk of bias was assessed using RoB 2. We report mean differences (MDs) with 95% credible intervals (CrIs) versus TT, ranking probabilities, and subgroup NMAs by learner group. RESULTS: Thirty-one RCTs (n = 3439) were included; 15 contributed complete data to the network. Risk of bias was low in 15 trials and raised some concerns in 16. The network was modest for knowledge (11 trials) and sparse for attitudes (3) and practice (4). Compared with TT, metaverse-based learning showed improved attitudes (MD 15; 95% CrI 12 to 18), based on a single trial. For knowledge and practice, Kahoot-based quizzes (MD 9.1; 95% CrI -8.9 to 27) and app-based learning (MD 4.6; 95% CrI -4.4 to 14) had the highest estimated mean improvements, but credible intervals were wide and included the null for most comparisons. Subgroup rankings differed by learner group, but several comparisons were imprecise and uncertainty was substantial, particularly in sparse networks. CONCLUSIONS: GBL modalities may improve learning outcomes compared with TT, but relative effects appear domain-specific and the certainty of rankings is limited by sparse evidence and imprecision. Future trials should prioritise head-to-head comparisons, robust outcome measurement, and longer-term retention and transfer outcomes in both student and in-service populations.

Humans↗