SEARCH · Search PubMed

Results for “learning disability”

Search indexed PubMed citations on genomics, clinical trials, systematic reviews and public health. Explore titles, authors and supplied subject terms, then open the PubMed record.

SourceResearch connectionFrom yearThrough year

Quote a phrase for an exact phrase match. Source license links do not imply unrestricted reuse.

103 recordsLinked to original sources

PubMed2026

What Constitutes Effective Support and Provision Within Day Service Centres for People With Intellectual Disabilities? A Systematic Review of Qualitative Research.

BACKGROUND: This review aimed to investigate the effectiveness and quality of support and provision within day service centres for people with intellectual disabilities. METHOD: The International Bibliography of the Social Sciences, Scopus and PsycInfo databases were searched in August 2024, and the results were reported according to the PRISMA guidelines. Peer-reviewed, English-language, qualitative studies that investigated the effectiveness of day service provision for people with intellectual disabilities in non-residential settings were considered for review. Methodological quality of the included studies was assessed using the JBI Critical Appraisal Tool for qualitative research. Qualitative themes were identified through thematic analysis and synthesised using the ConQual approach. RESULTS: Fourteen studies were included and four key themes emerged: 'perceptions of service quality'; 'community-orientation, integration, and empowerment'; 'challenging behaviours and safety'; and 'staff-centred factors and job satisfaction'. Confidence in the evidence was 'very low' for 3/4 themes, while there was 'moderate' confidence in the evidence related to the theme 'perceptions of service quality'. CONCLUSIONS: Day service centres for people with intellectual disabilities may enhance their effectiveness and quality of provision by concentrating on promoting communication, engagement, relationships, social networks and community integration. Addressing the methodological shortcomings and incomplete reporting of related research in future would contribute to improvements in overall confidence in the evidence base. This can then be better used to inform and further enhance day service provision for people with intellectual disabilities.

Humans↗

PubMed2025

Diagnostic and clinical utility of exome sequencing and chromosomal microarray in children with GDD/iD: a meta-analysis.

BACKGROUND: Global developmental delay/intellectual disability (GDD/ID) is among the most common neurodevelopmental disorders, with up to half of cases are attributed to genetic factors. Chromosome microarray (CMA) has traditionally been the primary genetic test for idiopathic GDD/ID. However, whole exome sequencing (WES) and whole genome sequencing (WGS) have recently emerged, substantially increasing diagnostic yields in these populations. METHODS: We conducted a comprehensive literature search of PubMed, Scopus, EMBASE, and the Cochrane Library from inception to April 29, 2025. Studies reporting the diagnostic utility of these tests in children with GDD/ID were included and analyzed. RESULTS: A total of 102 studies, comprising 55,752 children, were reviewed. The pooled diagnostic yield of WES was 0.37 (95% CI: 0.33-0.41; I2 = 93%), significantly higher than that of CMA at 0.19 (95% CI: 0.16-0.21; I2 = 95%). Subgroup analyses showed that WES yielded significantly higher diagnostic rates than CMA in both same-sample comparisons (OR = 2.27, 95% CI: 1.08-4.78) and different-sample comparisons (OR = 1.65, 95% CI: 1.15-2.37). Only one study evaluated WGS, reporting a diagnostic yield of 0.27. Meta-regression revealed a significant association between CMA diagnostic yield and the proportion of male participants (p < 0.01), but not with WES. No significant difference in diagnostic utility was observed between isolated GDD/ID and GDD/ID with comorbidities. CONCLUSION: In children with unexplained GDD/ID, WES demonstrates superior diagnostic and clinical utility compared to CMA. Incorporating WES as a first-line investigation in the diagnostic evaluation of GDD/ID may be warranted.

Humans↗

PubMed2026

The effect of combining visuo-vestibular exercises with manual therapy and exercise on sensorimotor function in chronic neck pain: A randomized controlled trial.

OBJECTIVE: To investigate whether adding visuo-vestibular exercises to standard manual therapy and exercise produces superior improvements in sensorimotor function, pain, balance, and functional disability in adults with chronic neck pain. METHODS: This prospective, randomized controlled trial enrolled 58 adults with chronic neck pain (≥3 months) allocated to a manual therapy and exercise group (MtE; n = 29) or MtE plus visuo-vestibular exercises (MtE-VVE; n = 29). Both groups completed 12 supervised sessions over six weeks with a daily home exercise programme. Outcomes were assessed at baseline, 6 weeks, and 12 weeks, and included pain intensity (Visual Analog Scale [VAS]), upper extremity reaction time, computerized posturography, the Neck Disability Index (NDI), and cervical muscle endurance. RESULTS: Fifty-four participants (27 per group) completed the study. Both groups improved significantly across all outcomes (p < 0.001). At 12-week follow-up, the MtE-VVE group demonstrated superior outcomes: activity-related pain was reduced by an additional 2.00 cm (95% CI: 0.75-3.25; p = 0.005), bilateral reaction time improved by 1.70 s (p = 0.001), eyes-open mediolateral sway decreased by 0.50 mm (p < 0.001), NDI score was 6.30 points lower (95% CI: 3.42-9.18; p < 0.001), and cervical flexion and extension endurance improved by 12.00 s and 27.70 s, respectively (p ≤ 0.020). CONCLUSION: Adding visuo-vestibular exercises to standard manual therapy and exercise produces clinically meaningful and sustained improvements in activity-related pain, sensorimotor function, postural control, and functional disability in adults with chronic neck pain, and may be recommended as an effective adjunctive intervention.

Humans↗

PubMed2026

Navigated repetitive transcranial magnetic stimulation for post-stroke recovery: A systematic review and meta-analysis of randomized controlled trials.

Repetitive transcranial magnetic stimulation (rTMS) is a subcategory of non-invasive brain stimulation (NIBS), used to modulate brain plasticity and improve post-stroke recovery. Neuronavigation is used to improve the accuracy of stimulation with the aim of achieving a superior clinical outcome than with conventional targeting. The objective of this review is to evaluate the efficacy of navigated rTMS in subacute and chronic stroke patients in comparison to sham stimulation. We conducted a systematic-review and meta-analysis of randomized controlled trials (RCTs) identified from Pubmed, Scopus and Cochrane CENTRAL. Trials employing neuronavigated rTMS were included of these five types; high and low frequency rTMS, intermittent and continuous theta-burst stimulation (TBS) and Hebbian-type stimulation. 13 RCTs were included after a screening of 1900 studies. 606 patients receiving either active (n = 360) or sham stimulation (n = 246) were assessed. The pooled standardized mean difference (SMD) favored rTMS over sham SMD = 0.4 (95 %CI: 0.11-0.69), with moderate heterogeneity I2 = 55 %. Among stimulation modalities, continuous TBS showed the largest pooled effect. rTMS was also associated with significant improvements in disability-related outcomes, SMD = 0.61 (95 % CI 0.14-1.08). Navigated rTMS is associated with modest but significant improvements in motor and disability outcomes in subacute and chronic stroke. Large comparative trials are required to clarify the potential added value over conventional targeting approaches.

Humans↗

PubMed2026

Effects of Dynamic Neck Sensorimotor Biofeedback Training in Individuals With Mechanical Neck Pain: A Pilot Randomized Controlled Trial.

Mechanical neck pain (MNP) is commonly accompanied by pain-related functional limitations, sensorimotor disturbances, and fear of movement, which together may contribute to persistent disability. This preliminary randomized controlled trial study investigated the short-term effects of dynamic neck sensorimotor-based biofeedback training in individuals with MNP. 20 MNP patients from outpatient clinics were assigned to a biofeedback training group or a control group. The training group underwent dynamic biofeedback exercises twice weekly for 2 weeks, whereas the control group performed repeated cervical movements without biofeedback. Outcomes included cervical kinematics as repositioning errors (RPE), movement units (MU), maximal range of motion (ROM), and subjective measures, including pain intensity, Neck Disability Index (NDI), and Fear-Avoidance Beliefs Questionnaire (FABQ). All participants completed post-intervention assessments; adherence in the training group was 100%, with no missing data and no adverse events reported. Within the biofeedback training group, participants receiving biofeedback training demonstrated greater improvements in cervical repositioning accuracy during flexion (51.95%, p = 0.04) and extension (46.67%, p = 0.02), along with reductions in fear-avoidance beliefs related to physical activity and work (p < 0.05); these changes were less apparent in the active control group. Exploratory regression analyses suggested associations between improvements in repositioning accuracy and pain reduction, and between increased cervical range of motion and improvements in fear-avoidance beliefs related to physical activity. These pilot findings suggest that dynamic sensorimotor biofeedback training may improve proprioceptive acuity and fear-avoidance beliefs in individuals with MNP, supporting further evaluation in an adequately powered randomized trial.

Humans↗

PubMed2026

Adding Rib Mobilization to Diaphragm Release Techniques in Patients With Non-Specific Neck Pain: Randomized Controlled Trial.

BACKGROUND: Non-specific neck pain (NSNP) is a frequent issue that can negatively affect both mobility and function. Recently, there has been growing interest in newer therapeutic approaches, including rib mobilization and diaphragm release techniques, as potential ways to address NSNP and support better outcomes for those affected. PURPOSE: To find out the immediate effects of how (DRT) combined with (RMT) affects the level of pain and the extent to which patients' functional abilities are improved in cases of NSNP. METHODS: For this prospective RCT, 96 participants aged 20 to 45 years were randomly assigned to one of three equal groups based on their pain score (VAS). Group B engaged in (DRT) for 40 minutes, three times weekly for 8 weeks, in contrast to Group A, which got both RMT combined with DRT. Group C (active control) received advice and some exercises. Measurements were collected before and after the intervention; the primary outcomes included pain severity, evaluated using a visual analog scale (VAS); active neck range of motion (ROM), measured with a cervical range of motion (CROM) device; and neck flexion endurance. Additionally, the secondary outcome of neck-related disability was assessed using the Neck Disability Index (NDI). RESULTS: No statistically significant difference was identified among the three groups at baseline; nevertheless, a treatment effect emerged after 8 weeks (p = 0.001 and f-value = 4.15, ƞ2 = 0.306). A statistically significant time-treatment interaction was seen when comparing the pre- and post-treatment periods in groups A and B (p = 0.001, f-value = 3.16, ƞ2 = 0.251). CONCLUSION: The addition of rib mobilization to diaphragm release techniques in patients with non-specific neck pain resulted in statistically significant improvements in pain intensity, cervical flexion, right lateral rotation, left lateral rotation, right rotation, and neck flexor endurance, with moderate to large effect sizes for pain reduction and cervical motion. However, no statistically significant differences were observed between groups for cervical extension, left rotation, or the Neck Disability Index (NDI), and only a marginal clinical improvement in NDI was noted in Group A. The observed benefits in the combined intervention group may not be attributable solely to rib mobilization. The increased treatment complexity and greater therapist interaction inherent in the combined approach could also have influenced the outcomes. TRIAL REGISTRATION: ClinicalTrials.gov identifier: NCT07133646.

Humans↗

PubMed2026

ORBIT: Oncogenic Representation Learning via Bi-Prototype Contrastive Learning in Hyperbolic Space for cancer driver gene identification.

Accurate identification of cancer driver genes is crucial for precision oncology but remains challenging due to the complexity of integrating heterogeneous data and modeling dynamic biological systems. To address these limitations, we propose ORBIT (Oncogenic Representation Learning via Bi-Prototype Contrastive Learning in Hyperbolic Space). Our framework synergistically fuses multi-omics profiles with functional network data using a context-adaptive graph reweighting mechanism to capture cancer-specific dynamics. The model employs a bi-prototype contrastive learning strategy within hyperbolic space, which aligns gene representations around distinct driver and non-driver semantic anchors while preserving the intrinsic hierarchy of biological networks. Comprehensive evaluations demonstrate that ORBIT achieves highly competitive stability in pan-cancer analysis while consistently outperforming state-of-the-art methods in cancer-specific predictions. Furthermore, functional enrichment analysis confirms that the model effectively segregates core cancer pathways, and drug sensitivity profiling validates the clinical relevance of the identified drivers. By integrating hyperbolic geometry with context-adaptive learning, ORBIT offers a robust and interpretable paradigm for precision medicine. The source codes and datasets are publicly accessible at https://github.com/spcho-dev/ORBIT.

Humans↗

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

Reinforcement learning-based dynamic ensemble for missense variant effect prediction and tiered prioritization of VUS.

BACKGROUND: Accurate classification of missense variants remains a challenging task despite major advances in genomics. Numerous computational models have been developed to assist in variant classification, but often require repeated integration and benchmarking efforts. Ensemble methods have been proposed to overcome the limitations of single predictors, but mostly rely on fixed, predefined weights that constrain their ability to capture interactions among predictive signals. METHODS: We present GenixRL, a dynamic ensemble framework that reformulates model fusion as a reinforcement learning optimization problem. GenixRL uses a Q-learning agent to learn a policy that dynamically weights the probabilistic outputs of complementary predictors, including BayesDel (addAF and noAF), ClinPred, and MetaRNN. Replacing static weighting with policy learning allows GenixRL to adaptively identify optimal weightings and substantially improve classification accuracy. RESULTS: In benchmark evaluation against 25 state-of-the-art predictors, GenixRL achieved an AUROC of 0.9644 on an independent ClinVar dataset. On saturation genome editing assays for BRCA1 and BRCA2, GenixRL achieved the best performance and ranked highest on 14 of 17 clinically significant genes in a zero-shot evaluation. Applied to uncertain and conflicting ClinVar variants, GenixRL enabled tiered, evidence-based prioritization of hundreds of thousands of variants as likely pathogenic or pathogenic with high confidence, supported by orthogonal population evidence from gnomAD. CONCLUSION: GenixRL advances pathogenicity prediction for missense variants and provides an adaptive ensemble that sorts variants of uncertain significance into tiered candidates for expert curation and functional validation.

Mutation, Missense↗

PubMed2026

A Meta-learning-driven strategy for adulteration detection in sweet potato starch and vermicelli using Raman spectroscopy.

To address the widespread adulteration of sweet potato starch and its vermicelli with cheaper starches and overcome conventional supervised learning's dependency on large labeled datasets, this study developed a few-shot discrimination method integrating Raman spectroscopy with meta-learning. We constructed a meta-learning framework using cassava- and wheat-adulterated sweet potato starch as the source domain for training, with potato-adulterated sweet potato starch and cassava-adulterated sweet potato vermicelli as two target domains for testing. Raman spectra showed high consistency between sweet potato vermicelli and its raw starch, laying the foundation for cross-domain detection. Testing yielded comprehensive classification accuracies of 95.33% and 98.00% for the two target domains, significantly outperforming SVM, RF, and CNN (max. 85.24%). This approach effectively identifies subtle starch variety differences in complex adulteration, providing novel food quality inspection solutions and verifying the feasibility of raw material-to-finished product cross-domain detection.

Ipomoea batatas↗

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

A systematic literature review of cultural concepts taught by pharmacist preceptors during pharmacy student experiential placements.

BACKGROUND: Cultural concepts such as cultural intelligence, awareness, competency and safety are essential in guiding culturally responsive care in health professional practice. Pharmacist preceptors play a pivotal role in sharing both clinical and cultural safe practice with pharmacy students. Culturally responsive care can contribute to achieving health equity, which is especially important for Indigenous communities. AIM: To review literature on cultural concepts in pharmacist preceptorship practices, and how these concepts are taught and communicated to pharmacy students during experiential learning. METHOD: The systematic review followed the PRISMA 2020 guideline. Scopus, PubMed, and Google Scholar were used to identify articles specific to pharmacist preceptors and pharmacy students published between 2015 and 2025, and available in English. RESULTS: Three full-text articles met the inclusion criteria. Major themes and subthemes were identified; pharmacist preceptors lacked preparedness to teach cultural concepts, resulting in variability in preceptors' understanding of cultural concepts and confidence in fulfilling preceptor responsibilities, underutilised structured frameworks to guide students' learning, challenges with preceptorship due to limited resources and support, and the influence of preceptorship on student learning, which impacted students' learning and competency. CONCLUSION: Pharmacy students had minimal exposure to culturally informed pharmacist preceptorship. It is likely that pharmacist preceptors require country-specific educational resources to support culturally safe preceptorship. Future research is required to substantiate these findings, and to guide culturally responsive practice and promote equitable health outcomes in diverse populations.

Humans↗

PubMed2026

Systematic evaluation of one-dimensional-to-two-dimensional near-infrared spectroscopy transformations with deep learning for quantifying coconut sap adulteration.

Near-infrared (NIR) spectroscopy have limitations when combined with deep learning (DL) algorithms because they rely on low-dimensional datasets. Therefore, we investigated the potential of transforming one-dimensional (1D) NIR spectra into two-dimensional (2D) spectrograms using synchronous and asynchronous techniques and the continuous wavelet transform (CWT) and their effectiveness by integrating with DL for detecting adulteration in coconut sap. NIR spectra (12,500-4000 cm-1) were collected from binary mixtures (0%-100%;w/w). The performance of all DL (convolutional neural networks-CNN, AlexNet and ResNet) models was compared with that of partial least squares (PLS). The models were ranked in the mentioned order based on their performances: 2D-CWT > 2D-asynchronous > 2D-synchronous > 1D/2D-PLS. The important features of the best model can be explained and visualized using gradient-weighted-class-activation-mapping. The findings highlight that the 1D-to-2D NIR data transformation combined with DL is a highly robust approach because it addresses the feature representation gap in NIR data and effectively captures the spatial-spectral correlations.

Spectroscopy, Near-Infrared↗

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-assisted Mn-N-C nanozyme colorimetric sensor array for trace-level detection of biogenic amines in meat.

Accurate detection of biogenic amines (BAs) in meat remains challenging due to their high structural similarity and co-occurrence. Herein, an Mn-N-C nanozyme was synthesized via a metal-organic framework confined pyrolysis strategy, possessing excellent oxidase (OXD)- and peroxidase (POD)-like activities. The dual enzyme-like activity showed Km values of 0.1584 mM (OXD) and 0.1498 mM (POD), respectively, in detection system. Leveraging these properties, a colorimetric sensor array was constructed, enabling the detection of four representative BAs within a concentration range of 2-10 ppm with 100% classification accuracy. In addition, a concentration independent recognition model based on an artificial neural network was developed to address signal nonlinearity interference in meat. The integrated system achieved accurate trace-level identification of BAs in perishable fish, pork, and chicken, demonstrating its applicability for early-stage BAs monitoring and quality deterioration warning during storage and transportation.

Biogenic Amines↗

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↗