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PubMed2026

Associations Between Health-Related Physical Fitness and Accelerometry-Based Energy Expenditure in Physiotherapy Workers.

BACKGROUND AND PURPOSE: Although it has been assumed that higher physical activity (PA) levels will contribute to better physical fitness (PF) performance, the interplay between these two has yet to be investigated. Moreover, the majority of studies have been presented in children and adolescents, and older adults, while little is known about the correlation in the adult working population of physiotherapists. Therefore, the main purpose of the study was to examine associations between objectively measured PA and health-related PF. METHODS: We recruited 50 physiotherapists (72.6% women) from several public and private settings in the city of Zagreb. The SenseWearArmbandPro3 (SWA), a triaxial accelerometer placed on the nondominant hand for 7 consecutive days, was used to capture total energy expenditure (TEE) and active EE (AEE). Cardiorespiratory fitness included the Harvard step test, and muscular fitness was composed of sit-ups in 60 sec and the Handgrip strength. Flexibility was evaluated using the Toe-touch test. RESULTS: TEE and AEE were moderately and positively correlated with the Harvard step test (r = 0.65 and 0.62, p < 0.001), sit-ups (r = 0.70 and 0.59, p < 0.001), and the Handgrip strength test (r = 0.74 and 0.64, p < 0.001). No significant correlation with the Toe-touch test was observed (r = -0.25 and -0.19, p > 0.05). When models were adjusted for age, weaker, but significant positive correlations remained. DISCUSSION: The findings suggest that both cardiorespiratory and muscular fitness are positively associated with PA, whereas no statistically significant association with flexibility was detected. Thus, it is not surprising that we obtained moderate to almost strong correlations between TEE and AEE with cardiorespiratory and muscular fitness. CONCLUSIONS: In physiotherapists, TEE and AEE yield moderate correlations with health-related PF, especially for cardiorespiratory and muscular fitness.

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PubMed2026

Age at menopause and subjective cognitive symptoms predict digital cognitive outcomes at the gynecological Well-Woman visit.

INTRODUCTION: Women are at increased risk for Alzheimer's Disease (AD). Growing evidence suggests that the menopausal transition may represent a vulnerable window for development of AD-related pathology. Yet, women are diagnosed with AD later than men. Conducting routine cognitive screenings and integrating information about both cognitive symptoms and age at menopause may help address sex-based disparities in detection and prevention. This study investigated whether subjective cognitive symptoms, in combination with age at menopause, were associated with performance on a digital cognitive task in postmenopausal women. METHODS: 183 postmenopausal women (mean age = 63.8, range = 45-85) were recruited after their Well-Woman visit. Participants completed the Screener for Cognitive Problems in Everyday Life (SCoPE) to assess subjective cognitive symptoms, followed by a sensitive measure of objective cognition: the Linus Health Digital Clock and Recall (DCR™). Information was also collected on age at menopause. We examined associations of subjective cognitive symptoms and age at menopause with digital cognitive performance, adjusting for age, education and depression. Model fit was evaluated using adjusted R2, AIC, and BIC. RESULTS: 48.1% of women reported one or more cognitive symptoms on the SCoPE. On objective testing, 73.2% scored in the normal range, 20.8% in the borderline range, and 6.0% in the impaired range. SCoPE total score was negatively associated with objective cognitive performance in adjusted models (B = -.12, p = .03). Age at menopause showed a significant quadratic association with cognitive performance (B = -0.006, p<.001). SCoPE total was not associated with DCR subtests, while age at menopause predicted both Delayed Recall and Clock Drawing. CONCLUSION: Subjective cognitive symptoms and age at menopause were associated with lower performance on a sensitive, objective cognitive test. Findings support routine cognitive screening and suggest that subjective cognitive symptoms as well as age at menopause are associated with cognitive function.

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PubMed2026

Machine learning vs. traditional methods for predicting postoperative cardiac complications after non-cardiac surgery: a systematic review and Bayesian network meta-analysis.

INTRODUCTION: Accurate prediction of peri-operative cardiac complications is critical to optimise pre-operative decision-making. Traditional risk prediction scores, such as the Revised Cardiac Risk Index, show only modest discrimination. Machine learning can model complex, non-linear relationships but their predictive performance compared with traditional scores remains unclear. METHODS: We performed a systematic review and Bayesian network meta-analysis. The primary outcome was postoperative adverse cardiac events following non-cardiac surgery. Prediction models were assessed relative to the Revised Cardiac Risk Index. As many studies evaluated multiple versions of each model type, the highest performing ('best version') and lowest performing ('worst version') results were analysed. Models were ranked using the surface under the cumulative ranking curve (SUCRA). RESULTS: Thirteen studies evaluating 54 models and 927,113 patients were included. Machine learning approaches generally outperformed traditional risk scores. Automated machine learning ranked highest (SUCRA 96.6) showed the greatest improvement in the best version analysis (mean difference (MD) 0.28 (95%CrI 0.16-0.40)) and remained superior in the sensitivity analysis (MD 0.30 (95%CrI 0.14-0.45)). Gradient boosting models showed superior performance over the Revised Cardiac Risk Index across analysis (best version: MD 0.20 (95%CrI 0.14-0.26), worst version: MD 0.18 (95%CrI 0.12-0.25), SUCRA 82.4). The Gupta Perioperative Risk for Myocardial Infarction or Cardiac Arrest score outperformed the Revised Cardiac Risk Index in the best version analysis (MD 0.16 (95%CrI 0.01-0.32)). Between-study heterogeneity was low. None of the included studies externally validated their machine learning models and only six were judged to be at low risk of bias. DISCUSSION: Most machine learning models showed better discrimination than traditional risk scores, with automated machine learning and gradient boosting models ranking highest. However, study quality, calibration reporting and absence of external validation limit immediate clinical adoption. Prospective, multicentre evaluation is required before integration of these models into peri-operative practice.

Humans↗

PubMed2026

Predicting ACL injury risk in athletes: A systematic review of machine learning-based models.

BACKGROUND: Early ACL injury risk identification in athletes is essential. This systematic review examines machine learning (ML) models for predicting ACL injuries, evaluating their methodological quality, performance, and reliability. METHOD: A comprehensive electronic search was conducted across PubMed, Scopus, Web of Science, and IEEE Xplore databases, supplemented by Google Scholar for grey literature, covering articles published between January 1, 2015, and August 30, 2025. Eligible studies were appraised using the Prediction Model Study Risk of Bias Assessment Tool (PROBAST) for methodological quality and risk of bias, and the Transparent Reporting of a Multivariable Prediction Model for Individual Prognosis or Diagnosis (TRIPOD) guidelines for quality of evidence. RESULTS: Ten studies were included. PROBAST showed eight studies had moderate risk of bias and two low risk. TRIPOD found only two studies met quality criteria. ML models included logistic regression (n = 5), support vector machines (n = 4), k-nearest neighbor (n = 3), decision trees (n = 3), random forests (n = 5), neural networks (n = 2), linear discriminant analysis (n = 1), and pre-trained CNNs (n = 1). AUC ranged from 0.63 to 0.98. Accuracy (reported in six studies) ranged from 26% to 95%; however, these values should be interpreted with caution due to the absence of confidence intervals, lack of class imbalance handling, and limited external validation across studies. Tree-based ensemble methods such as random forest achieved competitive accuracy (74-86%), while SVM, a non-ensemble classifier, reported accuracy ranging from 71% to 95%; however, the highest values were obtained in studies with notably small sample sizes (n = 12 to n = 39), raising concerns about overfitting and generalizability. CONCLUSION: Current ML algorithms show promise for identifying athletes at high ACL injury risk and detecting relevant risk factors. Although study quality was generally satisfactory, future research should prioritize external validation and model interpretability to support clinical translation.

Humans↗

PubMed2026

Not just when, but how: An exploratory dual-control approach to video feedback in motor learning.

The present study provides exploratory evidence for a novel dual-control paradigm. It examines whether combining temporal over video feedback timing with learner-controlled interactive playback functions (pause, slow-motion, rewind) would enhance motor skill acquisition beyond temporal autonomy alone. Sixty-four novice adults were randomly assigned to one of four conditions: Full Control (self-controlled timing + interactive replay), Partial Control (self-controlled timing + non-interactive replay), Yoked Full Control (externally controlled timing + interactive replay), or Yoked Partial Control (externally controlled timing + non-interactive replay). Motor accuracy (Radial Error), movement consistency (Bivariate Variable Error), technical execution, and self-efficacy were assessed at pre-test, 24-h retention, and 72-h retention following two acquisition sessions on a dart-throwing task (120 trials total). The Full Control group demonstrated the greatest and most durable learning gains across all outcomes. The Group × Time interaction was significant across all dependent variables (η2ₚ ranging from 0.140 to 0.234), with Full Control demonstrating superior retention at both 24 and 72 h relative to other groups (though differences relative to Partial Control were more pronounced at 72-h retention). Critically, the Yoked Full Control group showed comparatively weaker outcomes despite access to the same interactive playback functions. These findings suggest that interactive video tools may be most useful when learners can regulate both when feedback is accessed and how it is inspected. Theoretical and practical implications for the design of learner-centered video feedback systems are discussed.

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PubMed2026

GLP-1 Receptor Agonists and Musculoskeletal Outcomes: A Systematic Literature Review and Meta-Analysis.

INTRODUCTION: Glucagon-like peptide-1 receptor agonists (GLP-1 RAs) are increasingly used for the treatment of type 2 diabetes and obesity, but their effects on musculoskeletal health remain completely misunderstood. OBJECTIVE: This systematic review/meta-analysis aims to synthesise clinical data on the effects of GLP-1 RAs on key relevant bone, muscle, and joint outcomes. METHODS: MEDLINE, Cochrane Central Register of Controlled Trials (CENTRAL) (both via Ovid® platform) and Embase were searched from inception to March 2025 to identify relevant randomised controlled trials (RCTs) or real-world evidence (RWE) studies to be included. This bibliographic search was completed manually. A random-effect model meta-analysis was performed for any outcome reported in at least 2 studies. Subgroup analyses were performed on the type of GLP-1 RAs, type of comparator used and study design. Sensitivity analyses (i.e., leave-out sensitivity analyses and analyses restricted to the most adjusted effect estimate) were performed to test the robustness of the data. The strength of evidence was assessed using GRADE. This work has been performed in adherence with PRISMA statement. (PROSPERO Record ID: CRD420251024082). RESULTS: From 1148 potentially relevant references, 60 articles (46 RCTs, 13 RWE studies and 1 pharmacovigilance study, comprising 1,250,717 individuals) met our inclusion criteria. Different GLP-1 RAs were represented across the panel of studies, i.e., semaglutide, liraglutide, exenatide, dulaglutide, tirzepatide (dual agonist gastric inhibitory polypeptide [GIP]/GLP-1) and others. No effect on bone outcomes (i.e., bone mineral density [all sites] and fractures [all sites]) were observed when the meta-analytical models included the most adjusted effect size. Regarding muscle outcomes, a significant decrease of lean body mass/fat-free mass was consistently observed with GLP-1 RAs in the global model (k = 28, standardised mean difference [SMD] 0.52, 95% confidence interval [CI] -0.8; -0.23, I2 88%, p-value for heterogeneity <0.0001), which remained robust in all sensitivity analyses. Subgroup analyses showed that the effect was mainly driven by liraglutide and semaglutide, with a decrease in lean body mass/fat-free mass observed when GLP-1 RAs were compared with placebo. No publication bias was found. Regarding joint outcome, models revealed no significant change in The Western Ontario and McMaster Universities Osteoarthritis Index (WOMAC) pain, physical function and stiffness. CONCLUSIONS: This meta-analysis is the first to investigate the effects of GLP-1 RAs on a large panel of musculoskeletal health outcomes. While no significant effects were observed on bone- or joint-related outcomes, GLP-1 RAs were associated with reductions in lean body mass/fat-free mass, although the certainty of evidence was low and these changes appeared largely related to weight loss. Whether these changes translate into clinically meaningful impairments in muscle function or physical performance remains uncertain. Further studies in this field, including those looking at muscle function, strength or performance and using multivariate models considering confounding are needed to better reinforce the models and final findings.

Journal Article↗

PubMed2026

Frequency of Human Brucellosis Complications in West Asia: A Systematic Review and Meta-Analysis.

BACKGROUND: Brucellosis is a multi-systemic zoonotic infection. The West Asia/Middle East region is an important global hotspot for brucellosis. This systematic review and meta-analysis aimed to aggregate and synthesize all the available evidence regarding the complications of brucellosis in West Asia/Middle East region. METHODS: PubMed, Embase, Scopus, Web of Science, Google Scholar, and Proquest were searched. Selection of studies, data extraction, and the risk of bias assessment were performed in duplicate. Data extraction was performed for 254 complications. Meta-analysis was performed using a random-effects model with Freeman-Tukey double arcsine transformation. Where applicable small-study effects was assessed using funnel plots and Egger's test. Separate by-country, by-age, and by-publication-decade subgroup analyses were performed if feasible. RESULTS: Out of 9518 results, 240 studies (260 references) were included. The majority of the included studies were conducted in Turkey (n = 177). The reported complications varied and different complication categorization systems were detected. The highest pooled estimate was observed for musculoskeletal involvement (50%, 95%CI: 39%-61%, I2 = 96.63%). The evidences is up-to-date until March 4, 2024. CONCLUSIONS: Some complications such as the complications of the eye were not reported in all the countries. Therefore, it's recommended to determine the relative frequency of those complications in regions without such reports. The pooled estimates of different complications of brucellosis were different. There's a need for the standardization of the reporting of the complications of brucellosis to achieve comparability between studies and across different regions.

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PubMed2026

Pedagogical Efficacy of LLM-Generated Synthetic Data Versus Real-World Clinical Records: A Randomized Controlled Non-Inferiority Trial.

BACKGROUND: Expert-reviewed clinical cases generated by large language models (LLMs) may supplement case resources in medical education, but their short-term educational performance relative to real-case-derived teaching materials remains uncertain. We compared immediate post-training test performance after teaching with the two types of case materials and assessed non-inferiority against a prespecified margin. METHODS: We conducted a prospective, parallel-group, randomized non-inferiority trial. Through the Wenjuanxing online platform, participants were randomized 1:1 to learn with either real-case-derived teaching cases compiled by clinicians and reviewed by experts or AI-generated clinical cases produced by Gemini 3.0 Pro from fully de-identified matched real cases and reviewed by three senior general surgery specialists with full-professor rank. The primary outcome was the total score on an independent 10-item immediate post-training test (0-10 points), with a prespecified non-inferiority margin of -0.5 points. Secondary outcomes included the training-phase performance score, learning efficiency index, single-item mental effort rating, case realism, and case-source judgment. RESULTS: A total of 403 participants were randomized, of whom 386 were included in the modified intention-to-treat analysis: 192 in the real-case group and 194 in the AI-generated case group. The mean post-training test score was 4.95 (SD, 3.35) in the real-case group and 4.61 (SD, 3.35) in the AI-generated case group. The mean difference (AI-generated minus real-case group) was -0.335 points (95% CI, -1.006 to 0.337). Because the lower bound of the confidence interval was below the prespecified non-inferiority margin of -0.5 points, non-inferiority was not demonstrated (one-sided P = 0.314). No significant between-group differences were observed in the training-phase performance score, learning efficiency index, or single-item mental effort rating. AI-generated cases received lower realism ratings for Level 3 cases. The proportion of participants with at least one high-confidence completely incorrect response was 1.6% in the real-case group and 2.1% in the AI-generated case group. CONCLUSIONS: In this short-term, text-based online case-learning setting, no statistically significant between-group difference was observed in immediate post-training test performance; however, non-inferiority of AI-generated clinical cases relative to real-case-derived teaching materials was not demonstrated.

Humans↗

PubMed2026

Timing and Dose Matter: Late High-Speed Exposure and Higher High-Intensity Acceleration Volumes Reduce Hamstring Reinjury Risk in Elite Male Football (Soccer).

OBJECTIVES: The aims of this study were to (a) investigate whether the timing and magnitude of exposure to high-speed running (HSR), sprinting, and high-intensity accelerations during on-field rehabilitation after hamstring strain injury were associated with reinjury risk and (b) examine changes in match running performance upon return to play (RTP). DESIGN: Retrospective cohort study. METHODS: Data from 95 elite male football (soccer) players from five professional clubs competing in major European and Middle Eastern leagues were analyzed. Players with complete rehabilitation load profiles were included in the 2-month and 6-month reinjury analysis. Modified Poisson regression assessed associations between rehabilitation load characteristics and reinjury risk. Match running performance (HSR distance, sprint distance, and high-intensity accelerations per minute) during the five matches before injury and the first five matches after RTP was compared using paired t-tests for the entire cohort. RESULTS: Late introduction of HSR, sprinting, and high-intensity accelerations during rehabilitation (ie, ≥ 60% of rehabilitation progression) was associated with a significantly lower reinjury risk at 2 months (relative risk [RR] range = 0.948-0.969; P < .01) and 6 months (RR range = 0.964-0.979; P < .05). Higher daily exposure to high-intensity accelerations once introduced was protective (RR = 0.861 (0.778-0.951)). There were no meaningful associations between total volume of HSR or sprinting and reinjury. Match running performance metrics did not differ between pre-injury and post-RTP matches (all P > .05). Changes in performance were not correlated with rehabilitation load characteristics. CONCLUSION: The timing of high-intensity running exposure during on-field rehabilitation appeared associated with a lower hamstring reinjury risk. Delaying the introduction of HSR, sprinting, and accelerations, followed by a structured and progressive build-up, was associated with lower risk of reinjury without compromising the subsequent match performance of elite male football players. J Orthop Sports Phys Ther 2026;56(9):611-621. Epub 7 Jul 2026. doi:10.2519/jospt.2026.14077.

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PubMed2026

Predictive Models for Hypoglycemia Risk in Haemodialysis Patients With Diabetic Kidney Disease: Systematic Review and Meta-Analysis.

AIM: To provide evidence for selecting and developing reliable clinical assessment tools for hypoglycemia in diabetic kidney disease patients during haemodialysis. DESIGN: Review. METHODS: Systematic searches were performed in 9 Chinese and English databases to collect literature regarding the development of hypoglycemia risk prediction models in haemodialysis patients with diabetic kidney disease. Two reviewers independently performed literature screening, data extraction, risk-of-bias assessment, and applicability evaluation. The Prediction Model Risk of Bias Assessment Tool was used to assess the risk of bias and applicability of the included studies. Meta-analysis was conducted using R software. DATA SOURCES: CNKI, Wanfang, VIP, CBM, PubMed, Cochrane Library, EMbase, Web of Science, and CINAHL. The search period covered from the establishment date of each database to December 2025. RESULTS: Six studies, comprising six prediction models, were included. Two studies performed internal validation, and three conducted external validation. All models reported the area under the curve, ranging from 0.813 to 0.866, and calibration measures. Four studies were rated as having a high risk of bias, while all six demonstrated good overall applicability. The meta-analysis showed that the pooled AUC value of the six studies was 0.846 (95% CI: 0.823-0.867). CONCLUSION: Research on hypoglycemia risk prediction models in haemodialysis patients with diabetic kidney disease remains in the developmental stage. Although the included prediction models exhibited satisfactory apparent discriminatory ability and clinical applicability, most of the original studies suffered from a high risk of bias and lacked adequate validation. The true predictive performance and clinical application value of these models remain to be further verified. Accordingly, routine and unconditional clinical application is not recommended at this stage. Future studies should include more high-quality, multicenter external validation and develop models with high generalizability, favourable clinical applicability, and robust predictive performance to facilitate early identification of hypoglycemia risk in this population. IMPACT: This study systematically evaluated the hypoglycemia risk prediction models for diabetic kidney disease patients during haemodialysis, and the research on hypoglycemia risk prediction models for maintenance haemodialysis patients during dialysis is still in the development stage. This study provides a reference for clinical medical staff to select or develop hypoglycemia risk prediction and assessment tools for diabetic kidney disease patients during haemodialysis. REPORTING METHOD: This study was conducted in accordance with the relevant guidelines of the EQUATOR Network and followed the TRIPOD-SRMA Checklist. PATIENT OR PUBLIC CONTRIBUTION: No patient or public contribution. TRIAL REGISTRATION: PROSPERO: CRD420251243352.

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PubMed2026

Endoscopic Ultrasound-Guided Franseen Fine-Needle Biopsy for Solid Pancreatic Lesions: A Systematic Review and Meta-Analysis.

INTRODUCTION: Accurate tissue acquisition (TA) of solid pancreatic lesions is essential for guiding treatment with endoscopic ultrasound-guided fine-needle biopsy (EUS-FNB) being the preferred method. Among FNB designs, the three-pronged Franseen-tip needle demonstrates strong diagnostic performance, though direct head-to-head comparisons with other FNB designs remain limited. METHODOLOGY: This meta-analysis was conducted in accordance with PRISMA guidelines (PROSPERO: CRD420251123856). Eligible studies enrolled patients with solid pancreatic lesions who underwent EUS-guided FNB, directly compared the Franseen-tip with other FNB needles. Six databases were systematically searched through July 2025, and study selection, data extraction, and risk of bias assessment (QUADAS-2 tool) were performed independently by two reviewers. Pooled estimates were generated using random-effects and bivariate hierarchical models. RESULTS: Sixteen studies (2,010 Franseen vs. 2,811 comparator) were included. Bivariate analysis showed that sensitivity and specificity of the Franseen needle were comparable to newer-generation comparator needles (sensitivity 91.3% vs. 94.0%; specificity 99.99% vs. 99.15%), whereas older-generation needles demonstrated lower sensitivity (80.8%) and inferior discriminatory performance (Negative Likelihood Ratio [LR⁻] 0.19 vs. 0.09). Diagnostic accuracy was higher with the Franseen needle (RR 1.07, 95% CI 1.01-1.14; I2 = 69%). Sample adequacy was similar overall (RR 1.04, 95% CI 0.95-1.14) but superior to older-generation needles (RR 1.19, 95% CI 1.02-1.41) and in lesions > 30 mm (RR 1.14, 95% CI 1.02-1.28, I2 = 81.2%). The Franseen needle achieved nominally strong diagnostic performance (DOR 116.6), although small-study effects were observed. Primary procedural outcomes were comparable between Franseen and comparator needles, including technical success (RR 1.00, 95% CI 0.98-1.02) and histological core procurement (RR 1.04, 95% CI 0.92-1.17). The Franseen needle had fewer low-cellularity samples (RR 0.56, 95% CI 0.45-0.69) and lower specimen bloodiness (RR 0.48, 95% CI 0.25-0.90) but a slightly higher overall adverse event rate (RR 1.29, 95% CI 1.06-1.57). CONCLUSION: The Franseen needle provides superior diagnostic accuracy and sample adequacy compared to older-generation FNB needles with comparable performance to newer-generation designs. It reduces low-cellularity samples and specimen bloodiness, although adverse events are slightly increased, with other primary procedural outcomes remaining comparable. TRIAL REGISTRATION: PROSPERO (Registration No. CRD420251123856).

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PubMed2026

Evaluation of a cornea-specialized large language model for diagnostic and management accuracy in complex corneal cases.

PURPOSE: To evaluate whether a cornea-specialized large language model (LLM) enhanced with retrieval-augmented generation (RAG) improves clinicians' diagnostic and management accuracy in complex corneal cases compared to a general-purpose GPT-4o model and unaided clinician performance. METHODS: This prospective, randomized, masked evaluation study involved three cornea trainees who each independently reviewed 39 real-world corneal cases under three experimental conditions: unaided, GPT-4o-assisted, and assisted by a cornea-specialized GPT-4o model. The cornea-specialized model was constructed by embedding over 200 publicly available Wikipedia articles into GPT-4o's RAG framework. Participants provided open-ended diagnoses and selected the next-step management options (multiple choice). They were allowed up to three GPT-4o queries per case, and the AI-assisted arms were randomized to minimize bias. Accuracy for both tasks was compared against expert reference standards using McNemar's test. RESULTS: Diagnostic accuracy was 48.7%, 20.5%, and 38.5% unaided, improving to 69.2%, 46.2%, and 59.0% with general GPT-4o (p<0.04). The cornea-specialized GPT-4o further improved accuracy to 71.8%, 48.7%, and 74.4%, with improvements over unaided performance for all clinicians (p<0.01). For next-step decisions, unaided accuracy was 76.9%, 87.2%, and 59.0%. With the specialized model, Ophthalmologist 3 improved to 71.8% (p<0.05), Ophthalmologist 1 remained high at 82.1%, and Ophthalmologist 2 declined to 64.1% (p<0.05). CONCLUSIONS: A cornea-specialized LLM enhanced with RAG improved diagnostic accuracy in complex corneal cases, particularly among clinicians with lower baseline performance. Effects on management accuracy were inconsistent. Future studies should explore the use of open-ended management tasks and examine whether smaller, curated retrieval corpora yield better model performance.

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PubMed2026

Effectiveness of Yoga and Combined Exercise in Female With Rheumatoid Arthritis: Randomized Controlled Trial.

BACKGROUND: Although exercise is beneficial for Rheumatoid Arthritis (RA), the comparative efficacy of different modalities for patients in clinical remission remains unclear. This study compared the short- and long-term effects of yoga versus a combined exercise programme on pain, balance, mobility, fatigue, depression, and quality of life in females with RA in remission. METHODS: In this single-blind, randomized controlled trial, 74 female participants were allocated to yoga (n = 25), combined exercise (n = 25), or a usual care control group (n = 24). The intervention groups underwent an 8-week supervised programme. Clinical assessments, including the Visual Analogue Scale (pain), Berg Balance Scale, Timed Up and Go Test, Beck Depression Inventory, Fatigue Severity Scale, and Short Form-36, were conducted at baseline, post-intervention (8 weeks), and follow-up (20 weeks). RESULTS: Both intervention groups demonstrated significant improvements in all outcome measures compared with the control group at post-treatment and follow-up (p < 0.05). Notably, the yoga group exhibited superior outcomes compared to the combined exercise group in reducing pain intensity (median reduction of 4.00 vs. 2.00 points; p < 0.001, η2 = 0.724), as well as in physical function, balance, fatigue, depression, and quality of life at the 20-week follow-up. These benefits may be partly attributed to the incorporation of breathing and relaxation techniques inherent to yoga practice. CONCLUSIONS: Both 8-week yoga and combined exercise programs are effective in managing residual symptoms in females with RA in clinical remission. However, yoga appears to provide superior benefits in pain management and psychosocial well-being, supporting its integration into multidisciplinary RA management protocols, particularly for addressing psychosocial burden in patients achieving remission. TRIAL REGISTRATION: This study was retrospectively registered at NCT07072754 (clinicaltrials.gov).

Humans↗

PubMed2026

The role of artificial intelligence in the diagnosis and prognosis of traumatic brain injury based on brain CT scans: a systematic review.

Traumatic brain injury (TBI) is a leading cause of emergency department visits and a major contributor to injury-related mortality and long-term neurological disability. Non-contrast computed tomography (CT) is the gold-standard imaging modality for the rapid diagnosis of TBI. Clinical outcomes depend strongly on early detection and prompt acute management. Artificial intelligence (AI)-based models may support faster automated identification of traumatic findings and early prediction of patient prognosis. A systematic literature search was conducted in PubMed/MEDLINE, Scopus, IEEE Xplore, ACM Digital Library, and the Cochrane Library in accordance with PRISMA 2020 guidelines to evaluate AI-based models for automated detection of TBI-related findings on CT and for prediction of clinical outcomes. Risk of bias and applicability were assessed using QUADAS-2 for diagnostic accuracy studies and PROBAST + AI for prediction model studies. Twenty-two studies were included. Sixteen studies evaluated diagnostic tasks and 10 evaluated prognostic outcomes, with four studies contributing to both categories. Diagnostic performance was generally high, with many studies reporting AUC values approaching or exceeding 0.90, particularly for larger lesion volumes.Prognostic performance was more variable, with moderate to high discrimination and substantial heterogeneity. Only 9 studies incorporated independent external validation, and performance was frequently lower in external cohorts. All prognostic model studies were judged to be at high overall risk of bias using PROBAST + AI, and most diagnostic accuracy studies also demonstrated high or unclear risk of bias in at least one QUADAS-2 domain, most frequently in patient selection. AI-based models applied to brain CT demonstrate strong technical performance for both diagnostic and prognostic tasks in TBI. However, most studies relied on retrospective designs and lacked independent external validation which limits models generalizability and raises concern for potential overfitting. Prospective, multicenter studies with standardized methodologies and rigorous external validation are required before widespread clinical implementation.

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PubMed2026

Development and validation of a comprehensive prognostic model for 28-day ICU mortality in non-traumatic subarachnoid hemorrhage: an analysis based on the MIMIC-IV database.

BACKGROUND: Due to the complex pathophysiology of non-traumatic subarachnoid hemorrhage (SAH), accurate risk prediction remains a challenge. Our aim is to develop and validate a comprehensive prognostic model that integrates demographic characteristics, vital signs, laboratory parameters, and more, to provide clinical decision-making support in real-world practice. METHODS: We conducted a retrospective cohort study of 785 Non-traumatic subarachnoid hemorrhage patients. The cohort was randomly divided into a training set (n = 549) and a validation set (n = 236). Feature selection was performed using LASSO regression, followed by backward stepwise Cox regression for optimization. A nomogram was constructed based on independent predictive factors, and model performance was assessed using discrimination, calibration, and decision curve analysis. To prevent immortal-time bias, all predictors were anchored to a fixed early (first-24-hour) measurement window, treatment variables were modelled as binary indicators rather than cumulative exposures, and a five-model sensitivity analysis with baseline-severity adjustment was performed. RESULTS: The development of our model followed a systematic approach: first, 15 potential predictive factors were selected via LASSO regression, which were then refined to 12 independent predictors using backward stepwise Cox regression. The final predictive factors included: Ventilation, AHT, Nimodipine 60 mg, Age, SAPS.II, Input amount, Calcium total, Platelet count, White blood cells, Anion gap, pH, and Chloride. The integrated model demonstrated excellent predictive ability for 7-day, 14-day, and 21-day mortality in both the training set (AUC: 0.972, 0.934, 0.898) and the validation set (AUC: 0.968, 0.948, 0.911). Calibration curves and decision curve analysis confirmed the model's reliability and clinical utility across different time points. We constructed a nomogram for individualized risk prediction. Univariate Kaplan-Meier survival analysis demonstrated significant stratification of survival outcomes by each predictor, while restricted cubic spline analysis revealed non-linear relationships between continuous variables and mortality risk. Random survival forest analysis identified the top three predictive factors (Nimodipine 60 mg, Ventilation, AHT) and compared them with our full 12-variable model, confirming superior performance of the integrated model at all time points. At the 28-day primary endpoint, the model achieved a time-dependent AUC of 0.898 (training) and 0.904 (validation); after restricting predictors to the early baseline window, the leakage-controlled model retained good discrimination (validation C-index 0.803). CONCLUSIONS: Our ICU 28-day mortality prognosis model demonstrated robust performance in predicting ICU 28-day mortality in non-traumatic subarachnoid hemorrhage. The model, through the nomogram, provides individualized risk assessment, aiding clinical decision-making and patient stratification.

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PubMed2026

Longitudinal functional trajectory and surgical outcomes after intracranial meningioma resection: implications for surgical decision-making in older patients.

OBJECTIVE: As the population ages, meningiomas are increasingly encountered in older patients, yet longitudinal functional outcomes following surgery across age groups remain incompletely characterized. This study evaluated age-related differences in clinical and tumor characteristics, functional trajectory, and surgical outcomes. METHODS: This was a retrospective cohort study of 396 consecutive patients who underwent surgery for intracranial meningiomas at a single academic center between January 2023 and September 2025. Patients were stratified into 5 age groups (< 65, 65-69, 70-74, 75-79, and ≥ 80 years). Neurological deficits and Karnofsky Performance Status (KPS) were assessed preoperatively, at discharge, and at last follow-up. Logistic regression analyses identified predictors of prolonged length of stay (LOS) (> 5 days) and poor functional outcome at discharge (KPS < 80). RESULTS: Older patients presented with greater comorbidity burden, larger tumors, and lower preoperative KPS (all p < 0.05), while gross-total resection was achieved at comparable rates across all age groups (p = 0.504). A clinically meaningful inflection point was observed around age 75 years, with KPS < 80 at discharge rising from 7.4% and 9.7% in the < 65-year and 70- to 74-year subgroups and to 36.2% and 57.1% in the 75- to 79-year and ≥ 80-year subgroups (p < 0.001), and median LOS increased from 4 days in the younger groups to 9 and 7 days in the 75- to 79-year and ≥ 80-year groups (p < 0.001). However, recovery rates among patients who experienced functional decline at discharge were comparable across age strata. On multivariable analysis, independent predictors of prolonged LOS were age ≥ 75 years (OR 2.31, p = 0.019), diabetes mellitus (OR 2.85, p = 0.004), posterior fossa location (OR 2.1, p = 0.008), tumor diameter (OR 1.33, p < 0.001), postoperative edema (OR 2.58, p = 0.015), and neurosurgical complications (OR 3.18, p = 0.002). Independent predictors of poor functional outcome at discharge were age ≥ 75 years (OR 5.84, p < 0.001), lower preoperative KPS (OR 2.8, p < 0.001), posterior fossa location (OR 3.72, p = 0.003), neurosurgical complications (OR 3.56, p = 0.008), and recurrent meningioma (OR 2.89, p = 0.025). Among 70 endoscopic endonasal approach patients, higher preoperative deficit burden and subtotal resection rates were observed compared to open craniotomy, though overall functional outcomes were comparable. CONCLUSIONS: Surgical risk in meningioma resection increases from age 75 years onwards, yet recovery capacity following initial functional decline remains similar across all age groups. Preoperative functional status, tumor location, comorbidity burden, and recurrence history should guide surgical decision-making rather than age alone.

Humans↗

PubMed2026

Externally validated risk prediction models for gestational diabetes mellitus: A systematic review and meta-analysis.

INTRODUCTION: Risk prediction models for gestational diabetes mellitus (GDM) offer potential for early identification and targeted prevention. External validation is crucial to assess model performance across diverse populations. Despite the availability of numerous GDM prediction models, limited evidence exists on their external validation frequency, methodological quality, and clinical applicability. This systematic review evaluated externally validated GDM prediction models, focusing on methodological rigor, reporting standards, and clinical relevance to inform future research and implementation. MATERIAL AND METHODS: Databases including Ovid MEDLINE, Embase, Scopus, Emcare, and CINAHL were searched up to May 1, 2025. Studies reporting external validation of GDM risk prediction models were included. Two reviewers independently screened studies. Data were extracted using the CHARMS framework, and risk of bias and applicability were assessed using PROBAST+AI. The study protocol was registered in the International Prospective Register of Systematic Reviews (PROSPERO; CRD420251125758). RESULTS: Twenty-six studies validated 33 models, with validation sample sizes ranging from 50 to 75 161. Over half used the IADPSG criteria to define GDM. Discrimination metrics were commonly reported, but calibration, overall performance, and clinical utility were often lacking. Meta-analysis was feasible for only four models: Teede et al., Nanda et al., Naylor et al., and Van Leeuwen et al., each showing fair discrimination. The Teede et al. model was the most widely validated, with 11 external validations across six continents and a pooled AUC of 0.72 (95% CI: 0.67-0.76). Despite fewer validations, the Nanda et al. model achieved the highest pooled discrimination (5 validations; pooled AUC 0.77, 95% CI: 0.74-0.80). The Naylor et al. and van Leeuwen et al. models also underwent meta-analysis, as sufficient external validation studies were available to support comparative performance assessment. Notably, 69.23% of studies had a high risk of bias. CONCLUSIONS: While many models showed acceptable predictive performance, most validations were methodologically weak. Future studies should follow best-practice guidelines and promote scalable validation strategies, such as algorithm sharing, to enhance clinical utility.

Humans↗

PubMed2026

Influence of endodontic access on the fracture resistance, retention and microleakage of full-coverage restorations in vitro: A systematic review and meta-analysis.

BACKGROUND: Endodontic access through retained full-coverage restorations (FCRs) is a preferred option for patients because of its high cost-effectiveness. However, the clinical performance of FCRs after repaired access cavity remains insufficiently characterized. This systematic review investigates the effects of endodontic access cavity preparation through retained FCRs on fracture resistance, retention, and microleakage based on in vitro studies. METHODS: A comprehensive search was performed in PubMed, Web of Science, and Scopus databases. Studies investigating the influence of endodontic access on the fracture resistance, retention, and microleakage of FCRs were included. Two independent reviewers conducted study selection, data extraction, and risk-of-bias assessment using the QUIN tool. Meta-analysis was employed to estimate fracture resistance and retention, with sensitivity analysis and subgroup evaluation also performed. Microleakage was summarized qualitatively. RESULTS: Twentythree studies were included: fracture resistance (n = 15), retention (n = 5), and microleakage (n = 3). Endodontic access significantly reduced fracture resistance for zirconia (p = 0.0002) and lithium disilicate (LD) restorations (p = 0.007), but not for resin-matrix ceramic (RMC) restorations (p = 0.25). Abutment tooth type contributed to heterogeneity within the LD and RMC subgroups. Retention was significantly reduced when access cavities were left unrepaired (p = 0.03), whereas appropriate repair protocols restored or enhanced retention relative to baseline. Accelerated aging increased microleakage in retained FCRs. Surface pretreatments and flowable resin liners tended to reduce microleakage, but findings were inconsistent. CONCLUSIONS: Endodontic access significantly reduces fracture resistance of zirconia and LD FCRs, whereas RMC restorations show no significant change. Appropriate repair protocols can restore or improve retention, potentially exceeding original values. Limited evidence suggests that effective sealing is achievable with appropriate materials. However, well-designed and in-vivo researches are needed to provide more detailed clinical guidance. CLINICAL SIGNIFICANCE: When performing endodontic access through retained FCRs, reduced fracture resistance must be carefully considered for zirconia and LD restorations, while RMC restorations may be exempt from this concern. Loss of retention with access can be restored after repair. Surface pretreatment and flowable resin liners help decrease microleakage.

Humans↗