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PubMed2026

The incidence and descriptive factors of calcaneal malunion after surgical fixation of intra-articular calcaneal fractures using the sinus tarsi approach: A retrospective cohort study with binary logistic regression analysis.

BACKGROUND: This study evaluated the incidence of calcaneal malunion after minimally invasive sinus tarsi approach (MIS-STA) in displaced intra-articular calcaneal fractures (I-ACFs) and identified related descriptive factors of calcaneal malunion. METHODS: A retrospective review of 99 displaced I-ACFs treated with MIS-STA was conducted. Demographic data, pre-operative radiographs, and operative details were analyzed. Outcomes included numerical rating scale (NRS) pain scores at rest and during activities of daily living (ADL), Foot and Ankle Ability Measure (FAAM) for ADL and radiographic parameters. Logistic regression was used to identify descriptive factors associated with malunion. RESULTS: Malunion occurred in 33/99 cases (33.3%). The significant descriptive factors were the initial Böhler angle < 0.5 °, time to surgery > 12.5 days, and Sanders type ≥ III. Malunion patients had significantly worse NRS and FAAM scores (p ≤ 0.001). CONCLUSION: Calcaneal malunion after MIS-STA occurred in one-third of cases, with three descriptive factors identified and poorer outcomes observed. LEVEL OF EVIDENCE: III, Comparative retrospective study with binary logistic regression analysis.

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PubMed2026

Heterogeneity in Teriflunomide Treatment Arms: A Systematic Review and Meta‑Regression of Randomised Multiple Sclerosis Trials.

BACKGROUND: Teriflunomide is widely used as an active comparator in Phase 3 randomised trials for relapsing multiple sclerosis (RMS). Temporal changes in disease activity within teriflunomide-treated cohorts have not been systematically examined. OBJECTIVES: To assess temporal trends in relapse and disability outcomes across teriflunomide arms of Phase 3 multiple sclerosis (MS) trials and identify predictors of between-trial heterogeneity. METHODS: We performed a systematic review and meta-analysis of Phase 3 randomised controlled trials including a teriflunomide arm. PubMed, Scopus, and ClinicalTrials.gov were searched up to October 2025. Annualised relapse rate (ARR) and 12- and 24-week confirmed disability worsening (CDW) were extracted together with baseline characteristics. Risk of bias was assessed using the Cochrane Risk of Bias 2 tool. Random-effects meta-analyses, meta-regression, and sensitivity analyses were performed. RESULTS: Twelve teriflunomide cohorts from eight trials involving 4,900 adults with RMS were included. ARR ranged from 0.11 to 0.37 with substantial heterogeneity (I2 = 94%). Trial start year was inversely associated with ARR and explained a large proportion of between-study variability in exploratory meta-regression analyses. Confirmed disability worsening outcomes also showed substantial heterogeneity with a weaker trend toward lower event rates in more recent trials. CONCLUSION: Teriflunomide-treated trial populations have shifted toward lower relapse activity over time, and trial start year was the principal predictor of between-trial heterogeneity in ARR in exploratory analyses. These findings most plausibly reflect evolving recruitment and diagnostic practices rather than changes in drug efficacy. Accounting for these temporal dynamics is essential when interpreting outcomes from RMS trial using teriflunomide as comparator.

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PubMed2026

Quantitative assessment of the fingerprint evidential value using machine learning.

Fingerprints as physical evidence have long supported criminal investigation and adjudication. In practice, however, fingerprint identification relies mainly on examiners' experience. Furthermore, expert opinions tend to be categorical, even though the opinions with the same conclusion could differ substantially in evidential strength. To quantitatively assess fingerprint evidential value, this study proposes a machine learning-based framework as an interpretable decision-support tool. A lightweight residual one-dimensional convolutional neural network was constructed, incorporating channel recalibration and a similarity-driven attention mechanism to learn adaptive contribution weights for different matched minutiae (minutiae for short). Controlled experiments revealed that the predicted evidential value increased with the number of minutiae and was significantly influenced by the quality of minutiae. With 10 minutiae, the mean predicted scores were 4.49, 7.00, and 9.09 for blurred, moderately blurred, and clear minutiae, respectively. Multiple regression analysis indicated that replacing a pair of blurred minutiae with a pair of clear minutiae increased the score by 0.492, whereas replacing it with a pair of moderately blurred minutiae increased the score by only 0.216. By mapping predicted scores to graded levels of evidential strength, the framework contributes to a paradigm shift from categorical expert opinions to graded ones, helping courts evaluate fingerprint evidence more scientifically.

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PubMed2026

Are Adverse Childhood Experiences Associated with Metabolic Syndrome in Patients with Severe Mental Illness?

BACKGROUND: Patients with severe mental disorders (SMD) are at substantially elevated risk for metabolic syndrome (MetS), contributing to excess cardiovascular morbidity and premature mortality. Adverse childhood experiences (ACEs) have been associated with dysregulation of metabolic pathways, yet their contribution to MetS risk in SMD remains poorly understood. OBJECTIVE: This study aimed to investigate the association between ACEs and MetS in outpatients with bipolar disorder (BD) and schizophrenia (SZ) in clinical remission and to identify independent and incremental predictors of MetS using a hierarchical analytical framework. METHODS: This cross-sectional study included 140 outpatients with SMD (96 with BD and 44 with SZ) in clinical remission, recruited from a university hospital in Eastern Turkey. MetS was defined according to NCEP-ATP III criteria, and ACEs were assessed using the Turkish version of the Adverse Childhood Experiences Scale (ACE-TR). Hierarchical and multivariable logistic regression analyses were performed to examine factors associated with MetS. RESULTS: MetS was highly prevalent in this sample (46.4%). ACE-TR total score was independently and consistently associated with MetS across all hierarchical models (odds ratio [OR] range: 1.68-1.77), with each one-unit increase conferring approximately 71% higher odds in the fully adjusted model (OR = 1.71; 95% confidence interval [CI] 1.26-2.32; P = 0.001). The number of hospitalizations was the only other independently associated variable (OR = 1.19; 95% CI 1.02-1.39). Sexual abuse (16.9% vs. 2.7%; P = 0.004), emotional neglect (63.1% vs. 30.7%; P < 0.001), and physical neglect (30.8% vs. 14.7%; P = 0.022) were significantly more prevalent in the MetS group. ACE-TR total score was positively correlated with waist circumference and triglyceride levels. CONCLUSION: The strong and consistent association between ACEs and MetS underscores the importance of trauma-informed care models in psychiatric practice, where metabolic comorbidity remains a leading cause of premature mortality.

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PubMed2026

Prognostic value of the Island sign for hematoma expansion and functional outcome after intracerebral hemorrhage: a systematic review and meta-analysis.

PURPOSE: The Island Sign (IS) is a radiological finding observed in patients with intracerebral hemorrhage (ICH). This meta-analysis aimed to evaluate the association between IS and both hematoma expansion (HE) and functional outcomes by comparing ICH patients with and without IS. METHODS: We searched PubMed, Embase and Cochrane Library for studies of intracerebral hemorrhage reporting the IS. The primary outcomes were functional status and hematoma expansion, secondary outcome was mortality. Statistical analysis was performed using RStudio, effect sizes were calculated as odds ratios (ORs) with 95% confidence interval (95% CIs), and heterogeneity was assessed with I2 statistics. In addition, meta-regression and sensitivity analyses were performed, and publication bias was assessed through funnel plots and Egger's regression test. RESULTS: We included 21 observational studies with a total of 9,459 patients with spontaneous ICH, 1,769 of them had IS, while 7,690 did not. The mean age was 63.5 ± 13.2 and 5,835 (61.7%) were male. Poor functional outcomes (OR 2.77, 95% CI: 2.14-3.58, p < 0.0001, I2 = 4.9%) and hematoma expansion (OR 2.75, 95% CI: 1.87-4.03, p < 0.0001, I2 = 77.4%) were substantially higher in patients with IS, as well as the overall mortality rate (OR 2.54, 95% CI: 1.55-4.17, p = 0.0002, I2 = 0%). Meta-regression analysis showed no statistically significant association between imaging-related timing variables and hematoma expansion. Furthermore, the leave-one-out sensitivity analyses showed that no single study exerted a disproportionate influence on the overall effect for the examined outcomes, and Egger's linear regression tests were not statistically significant for both outcomes. CONCLUSION: Patients with the Island Sign are associated with higher rates of poor functional outcomes and hematoma expansion. Thus, IS is a relevant radiological finding with potential to support early risk stratification and optimize patient management and treatment selection.

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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

Cognitive behavioural therapy-based interventions on stress outcomes in pregnant women: A systematic review and meta-analysis.

BACKGROUND: Stress symptoms were the most common psychological problem in pregnancy. Cognitive behavioural therapy-based interventions are effective for antenatal depression and anxiety symptoms; but there are fewer studies for stress symptoms. OBJECTIVE: The review aims to (1) examine the effectiveness of cognitive behavioural therapy-based interventions in reducing stress outcomes (pregnancy-specific stress symptoms, generic symptoms, and objective stress) in pregnant women, and (2) identify significant moderators affecting the effectiveness of the intervention. DESIGN: Systematic review, meta-analysis, and meta-regression analysis of randomised controlled trials. METHODS: We conducted a three-step search (12 databases, 4 clinical registries, and citation searches) in English and Chinese up to July 24, 2025, by two independent reviewers. Meta-analysis, subgroup, and meta-regression analyses were performed using the R software. Quality assessment and certainty of the evidence were assessed with the Cochrane risk-of-bias tool version 2 and Grading of Recommendations, Assessment, Development, and Evaluation criteria. Publication bias was assessed using funnel plots and Egger's test. RESULTS: We included 20 randomised controlled trials involving a total of 6966 pregnant women from nine countries. Random-effects meta-analyses found that interventions significantly alleviated pregnancy-specific stress symptoms (Hedges' g = -0.84, 95% Confidence Interval, CI -1.42, -0.26, p < .01, I2 = 92.3%), reduced generic stress symptoms (g = -0.64, 95% CI -1.09, -0.20, p < .01, I2 = 87.4%) with median and large effect sizes at post-intervention. No effect was found in lowering cortisol levels (g = -0.99, 95% CI -2.58, -0.60, p = .12, I2 = 84%) at post-intervention. Subgroup and meta-regression analyses indicated that region, age of participants, use of intention-to-treat, missing data management analyses, frequency, modalities, and approaches of interventions, use of different comparators, and attrition rate were significant factors affecting the effectiveness of interventions. Subgroup analyses suggested that the intensity of intervention should be more than once per week using a blended mode among Asian populations. Multivariate meta-regression analyses indicated that both younger age (β = 0.13, p = .02) and a lower attrition rate (β = 0.03, p = .03) significantly improved the effectiveness of interventions. The overall certainty of the evidence was rated as either very low or low. CONCLUSIONS: Cognitive behavioural therapy-based interventions can supplement antenatal care to alleviate pregnancy-specific stress symptoms and generic stress symptoms, particularly in young Asian women. However, the evidence has some uncertainties. These findings should be interpreted with caution due to substantial heterogeneity. Well-designed trials on a large-scale with long-term follow-ups were needed. REGISTRATION: PROSPERO registration ID: CRD420251115913.

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PubMed2026

Risk factors and management strategies for needle disengagement from the visual field in pediatric robot-assisted laparoscopic pyeloplasty.

OBJECTIVE: This study aimed to identify risk factors for suture needle disengagement from the visual field during pediatric robot-assisted laparoscopic pyeloplasty (RALP) and propose effective strategies for prevention and management. METHODS: A retrospective cohort study analyzed clinical data from 339 pediatric patients who underwent RALP for ureteropelvic junction obstruction (UPJO) at a single institution between August 2017 and December 2020. Patients were categorized based on the occurrence of needle disengagement from the visual field. Various patient demographics and surgical procedural factors were evaluated. Univariate and multivariate logistic regression, along with LASSO regression, identified independent risk and protective factors. RESULTS: Needle disengagement occurred in 38 (11.21%) of 339 cases. Multivariate logistic regression identified five independent risk factors for needle disengagement: use of a 3-mm auxiliary trocar (OR = 4.69, 95% CI: 1.98-12.53, P < 0.001), non-standard needle holder use (OR = 2.32, 95% CI: 1.04-5.18, P = 0.038), unshaped suture needles (OR = 3.16, 95% CI: 1.44-7.19, P = 0.005), simultaneous use of ≥2 intra-abdominal sutures (OR = 2.46, 95% CI: 1.15-5.48, P = 0.023), and clamping the needle shank during withdrawal (OR = 3.42, 95% CI: 1.40-8.21, P = 0.006). Conversely, sufficient assistant experience (>10 cases) was identified as a protective factor (OR = 0.39, 95% CI: 0.18-0.88, P = 0.021). CONCLUSION: Suture needle disengagement from the visual field during pediatric RALP is associated with specific technical and instrumental factors. Implementing targeted strategies-such as mandating specialized needle holders, preoperative needle shaping, a single-needle workflow, prioritizing clamping the suture thread over the needle shank during withdrawal, and ensuring adequate assistant training-has the potential to significantly reduce significantly mitigate the risk of needle loss and enhance overall surgical safety in pediatric RALP.

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PubMed2026

A Dynamic Nomogram to Predict Metabolic Dysfunction-Associated Fatty Liver Disease in Patients with Metabolic Syndrome.

BACKGROUND: Metabolic syndrome (MetS) involves multiple metabolic disorders. This study aimed to identify high-risk populations for metabolic dysfunction-associated fatty liver disease (MAFLD) in patients with MetS and to establish a dynamic predictive nomogram. METHODS: A total of 627 patients with MetS from six regions in Zhejiang Province were enrolled and categorized into MAFLD and non-MAFLD groups, then randomly assigned to training and validation sets at a ratio of 7:3. Independent predictors of MAFLD were identified using least absolute shrinkage and selection operator regression and multivariable logistic regression analyses. These predictors were then used to construct a dynamic nomogram. RESULTS: A total of 627 patients with MetS were included in the final analysis, of whom 77.0% (483/627) were diagnosed with MAFLD. Multivariable logistic regression analysis identified body mass index (BMI), waist circumference (WC), total cholesterol (TC), alanine aminotransferase (ALT), MetS-defined dysglycemia, and education level as independent risk factors for MAFLD. MetS-defined dysglycemia showed the highest odds ratio (OR) for MAFLD development [OR = 1.87, 95% confidence interval (CI): 1.07-3.29]. Although the number of MetS components and the metabolic syndrome score were significantly associated with MAFLD in univariate analysis, they were not independently associated with MAFLD in the multivariate model. A dynamic nomogram for predicting MAFLD risk in patients with MetS was developed and internally validated. The area under the receiver operating characteristic curve was 0.834 (95% CI: 0.787-0.880) in the training set and 0.839 (95% CI: 0.771-0.899) in the validation set, indicating strong predictive performance. Bootstrap internal validation demonstrated good agreement between predicted and observed outcomes in calibration curves. Decision curve analysis further indicated favorable clinical applicability of the nomogram. CONCLUSION: BMI, WC, TC, ALT, MetS-defined dysglycemia, and education level are independent risk factors for MAFLD. A dynamic nomogram for predicting MAFLD risk in patients with MetS was successfully developed and validated.

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PubMed2025

Global epidemiology of diabetes and prediabetes in lean or non-obese patients with NAFLD: a systematic review and meta-analysis.

BACKGROUND: The presence of diabetes increases the risk of adverse outcomes of patients with non-alcoholic fatty liver disease (NAFLD) even in those with lean or non-obese NAFLD. However, the epidemiological data regarding the prevalence of diabetes and prediabetes in lean or non-obese NAFLD populations remain limited. We assessed the global epidemiology of diabetes and prediabetes in lean or non-obese patients with NAFLD. METHODS: Published studies were searched in PubMed, EMBASE, Cochrane Library, and Web of Science databases from the inception of the databases to October 2024. The pooled global prevalence of diabetes or prediabetes in patients with NAFLD was evaluated using random-effects meta-analysis. Subgroup meta-analysis and meta-regression were used to investigate potential sources of heterogeneity. RESULTS: A total of 54 studies involving 146,714 patients with non-obese or lean NAFLD were included. The pooled global prevalence of diabetes among patients with lean or non-obese NAFLD was 15.6% (95% CI 10.8%-22.7%). Studies from South America reported the highest prevalence (41.3%, CI 39.1%-43.5%). Meta-regression models showed that geographic region and mean age (p < 0.05) were associated with the were associated with the prevalence of diabetes, jointly accounting for 51.61% of the heterogeneity. The global prevalence of prediabetes among patients with lean or non-obese NAFLD was 22.9% (95% CI 12.5%-41.9%) with the highest prevalence reported in studies from Europe (34.4%, CI 23.0%-51.4%). Meta-regression models showed that geographic region and country (p < 0.05) were associated with the prevalence of prediabetes, jointly accounting for 73.65% of the heterogeneity. CONCLUSION: The pooled global prevalence of diabetes and prediabetes were 15.6% and 22.9% in lean or non-obese patients with NAFLD, respectively. These findings suggest the importance of diabetes screening in these patients.

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PubMed2026

Beyond predictive performance: A systematic review and critical methodological appraisal of AI/ML and conventional modelling strategies in breast, colorectal, and pancreatic Cancer.

BACKGROUND: Predictive modelling for cancer risk, treatment-related complications, and survival is central to precision oncology. Conventional logistic regression (LR) and Cox proportional hazards (CoxPH) regression remain widely used but are limited when modelling nonlinear interactions, high-dimensional imaging features, and multimodal clinical-metabolic predictors. Artificial intelligence (AI) and machine learning (ML) methods offer expanded capability through automated feature extraction, ensemble learning, and flexible survival modelling, but the evidence on when AI/ML adds value over conventional models across cancer sites and predictive tasks remains fragmented. OBJECTIVE: To systematically evaluate the methodological performance, validation strategies, and translational limitations of AI/ML models compared with conventional statistical models in published predictive-modelling studies for breast, colorectal, or pancreatic cancer. METHODS: PubMed, Scopus, and Web of Science were searched for studies published between January 2019 and March 2025. Two reviewers independently conducted title-and-abstract screening, full-text eligibility assessment, and PROBAST risk-of-bias assessment. Sixty-five studies (n = 907,567 participants) were narratively synthesised by cancer site, predictive task, model family, comparator, validation strategy, predictor modality, and calibration or explainability reporting. RESULTS: The 65 studies comprised breast cancer (n = 35), colorectal cancer (n = 21), and pancreatic cancer (n = 9). AI/ML superiority over LR and CoxPH was task- and data-dependent. CNN- and U-Net-based models predominated in imaging and body-composition tasks, tree-based ensembles consistently outperformed LR for tabular perioperative complication prediction, and CoxPH remained competitive, and in the largest pancreatic risk study, superior to XGBoost (C-index 0.802 vs 0.723) in well-structured datasets. PROBAST analysis-domain risk was moderate in 54 of 65 studies (83%), driven by limited external validation, sparse calibration reporting (11/65), and few decision-curve analyses (7/65). CONCLUSION: AI/ML adds the most methodological value in imaging-derived feature extraction and nonlinear perioperative prediction, while conventional regression remains preferable in large, structured datasets with linear predictors. Clinical translation requires standardised body-composition definitions, external validation, calibration assessment, decision-curve analysis, and explainability, in line with TRIPOD+AI and CLAIM standards.

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PubMed2026

Prevalence of Breastfeeding in Infants With Down Syndrome: A Systematic Review and Meta-Analysis.

AIM: To estimate the prevalence of breastfeeding-overall, exclusive, partial and depending on infants' age-in infants with Down syndrome, and to investigate associated factors. METHODS: A systematic literature search was conducted in Medline, Cochrane Library, Web of Science, Embase, CINAHL and SciELO up to 1 August 2024. Original articles that estimated the prevalence of breastfeeding in infants with Down syndrome, written in French, English or Spanish, were included. Study quality was assessed using the Joanna Briggs Institute (JBI) scale. Meta-analyses were performed for breastfeeding outcomes and meta-regression explored heterogeneity. The review was registered in PROSPERO (CRD42021278019). RESULTS: Twenty-six studies (3463 infants) were included. The estimated prevalence of overall breastfeeding regardless of duration was 71.6% (95% CI [60.3; 80.7]; 25 studies, 3351 infants) with high heterogeneity, I2 = 94%. The estimated prevalence of exclusive breastfeeding was 38.4% (95% CI [22.4; 57.3]; 10 studies, 1099 infants). No factor assessed in meta-regression was significantly associated with overall breastfeeding. CONCLUSION: The estimated breastfeeding prevalence in infants with Down syndrome is similar to that reported in the general population, despite high heterogeneity. Further studies using standardised methodology to assess breastfeeding barriers and facilitators in the context of Down syndrome would allow us to improve support for breastfeeding.

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PubMed2026

Associations between multiple essential trace metal concentrations and risk of hyperuricemia: insights from a central Chinese population.

Previous studies have indicated that levels of individual essential trace metals are related to hyperuricemia (HUA), but evidence on their combined effects is limited. To address this gap, the associations of individual and joint levels of 12 essential trace metals (manganese, selenium, nickel, chromium, cobalt, tin, iron, molybdenum, zinc, strontium, vanadium, and copper) with the risk of HUA were investigated in 2,021 adults recruited from Hunan Province, China. Inductively coupled plasma mass spectrometry (ICP-MS) was employed to determine urinary metal concentrations. Logistic regression, Bayesian kernel machine regression (BKMR), and quantile g calculation (Qgcomp) were applied to evaluate the associations of single and mixture metal concentrations with HUA. Of the participants, 516 (25.53%) were diagnosed with HUA. Inverse associations were found between vanadium, chromium, manganese, iron, cobalt, selenium, strontium, and molybdenum levels and HUA, with ORs ranging from 0.63 to 0.91. Conversely, a positive association was observed between zinc concentration and HUA [OR (95% CI): 1.17 (1.01, 1.37)]. Both BKMR and Qgcomp models showed a negative overall effect of essential trace metals on HUA risk, with strontium (- 43.6%) and vanadium (- 27.8%) being the main contributors. In addition, formal interaction tests revealed significant effect modification by age for tin and by BMI for zinc. In conclusion, the levels of essential trace metals were linked to a decreased risk of HUA, and these associations were modified by age and BMI only for specific metals.

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PubMed2025

Enhanced fracture detection on radiographs with AI assistance for clinicians: a systematic review and meta-analysis.

BACKGROUND: Emergency radiographic interpretation for fractures is prone to missed or misdiagnoses. Artificial intelligence (AI) is expected to become a powerful tool to assist clinicians in fracture detection. PURPOSE: A systematic review and meta-analysis was performed to assess whether AI improves clinicians' ability to detect fractures on radiographs. MATERIALS AND METHODS: A literature search was conducted in PubMed, Web of Science, and Cochrane Library for studies published between January 1, 2010, and October 10, 2025. A meta-analysis of diagnostic accuracy studies was performed using a Summary Receiver Operating Characteristic (SROC) curve. The quality of included studies was assessed using the Quality Assessment of Diagnostic Accuracy Studies 2 (QUADAS-2) tool. Subgroup analysis and meta-regression were conducted to explore potential sources of heterogeneity. RESULTS: A total of 26 studies were included . The pooled sensitivity of clinicians increased from 77% (95% CI: 72-81) to 87% (95% CI: 83-90) with AI assistance, while the pooled specificity improved from 88% (95% CI: 85-90) to 92% (95% CI: 89-94). The corresponding AUC values were 0.90 (95% CI: 0.87-0.92) before and 0.95 (95% CI: 0.93-0.97) after AI assistance. Eight studies were rated as high risk of bias. Subgroup analysis and meta-regression identified potential sources of heterogeneity, including fracture location, AI model type, high risk of bias, and reference standards. CONCLUSION: AI assistance significantly improves clinicians' diagnostic performance in detecting fractures on radiographs for extremity and trunk fractures.

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PubMed2026

Who Is the Ideal Candidate for High-Definition Liposuction? A Systematic Review and Meta-Analysis.

BACKGROUND: High-definition liposuction (HDL) enhanced muscular definition by selectively removing superficial subcutaneous fat. Despite its growing popularity and high satisfaction rates, no consensus exists regarding the ideal candidate. This systematic review evaluates demographic factors, patient selection criteria, and complication rates associated with HDL. METHODS: A systematic review and meta-analysis were conducted utilizing PRISMA-P guidelines. PubMed, Cochrane Library, Embase, and Web of Science were searched for studies published within the past 10 years. Seventeen studies met inclusion criteria, comprising 9570 patients. Study designs included randomized controlled trials, cohort studies, and observational analyses reporting demographics, procedural details, and complications. Standardized data extraction was performed. Pooled analyses were conducted for hematoma and seroma, and univariable meta-regression assessed associations between mean age, BMI, and proportion of female patients with complication rates. RESULTS: Mean age was 36.0 years and mean BMI was 25.8 kg/m 2 (range: 18 to 66); 55% of patients were female. Selection criteria varied; most studies applied BMI thresholds (≤30 to 34 kg/m 2 ) and excluded poorly controlled diabetes, heavy smoking, and thromboembolic history. The abdomen, flanks, and chest were most treated. Mean fat removal was 4268.8 mL, with an average operative time of 235 minutes. Complication rates were low; the pooled incidence of seroma was 7% and hematoma was 1%. Meta-regression showed no significant associations between complications and age, BMI, or sex. Patient satisfaction was high. CONCLUSION: HDL is safe and effective across diverse populations; however, variability in selection criteria and reporting limits evidence-based definitions of ideal candidates. Further prospective studies with standardized reporting are needed.

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PubMed2026

Prevalence of psychosis in South Asia: A systematic review and meta-analysis.

BACKGROUND: Psychotic disorders are a major contributor to global disability, yet prevalence data from South Asia which inhabits a quarter of the world's population, remain limited. Reliable estimates are essential for health service planning, policy, and closing the substantial treatment gap. This review provides the first comprehensive synthesis of psychosis prevalence across South Asia. METHODS: We searched PubMed, Embase, Web of Science, Global Health, and Medline to 18 December 2024 for DSM- or ICD-based prevalence studies in Afghanistan, Bangladesh, Bhutan, India, Maldives, Nepal, Pakistan, and Sri Lanka. Cross-sectional and longitudinal studies in community or clinical populations were included. Study quality was assessed using the Joanna Briggs Institute checklist. Random-effects meta-analyses estimated pooled prevalence using the logit transformation. Heterogeneity was explored with meta-regression of key methodological variables (publication year, diagnostic system, residential setting). FINDINGS: Thirty-one studies from five countries were included. Among community-dwelling adults, pooled point prevalence was 0.85% and lifetime prevalence was 1.40%, with inter-country differences (India 1.18%, Pakistan 2.13%, Nepal 2.90%). Clinical samples showed substantially higher proportions of individuals with psychosis in service settings (11.44%), reflecting concentration of cases in treatment-seeking samples. Data for children and adolescents were limited and summarised narratively. Heterogeneity was high across meta-analyses, and exploratory meta-regression did not identify any significant moderators. INTERPRETATION: Psychosis prevalence estimates in South Asia appear higher than global averages but should be interpreted cautiously due to substantial heterogeneity and methodological variation; nevertheless, they highlight the need for culturally sensitive screening, improved detection, and strengthened mental health services.

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PubMed2026

The association of 25-hydroxyvitamin D deficiency with neuroinflammation and prognosis in HIV-negative cryptococcal meningitis.

BACKGROUND: Cryptococcal meningitis (CM) in HIV-negative individuals is increasing, yet the role of vitamin D remains unclear. This study investigates serum 25-hydroxyvitamin D [25(OH)D] levels and their clinical implications in HIV-negative CM patients. METHODS: We conducted a retrospective case-control study of 93 HIV-negative CM patients and 191 healthy controls (HCs). Serum 25(OH)D levels, cerebrospinal fluid (CSF) fungal burden, cytokine profiles, the incidence of postinfectious inflammatory response syndrome (PIIRS), and one-year mortality were assessed. Bivariate logistic regression models identified predictors of mortality. RESULTS: CM patients had significantly lower serum 25(OH)D levels than HCs (18.33 vs. 23.69 ng/mL, p < 0.001), with a higher rate of deficiency (<20 ng/mL) in the CM group (59.14% vs. 34.03%, p < 0.001). Lower 25(OH)D levels were associated with elevated CSF levels of IL-6 and IL-8 (p < 0.05). Deficiency was linked to increased PIIRS incidence (43.64% vs. 21.05%, p = 0.028). Bivariate logistic regression showed a protective trend for 25(OH)D levels (OR 0.939, 95% CI 0.877-1.006, p = 0.075), although deficiency was not associated with higher mortality. CONCLUSIONS: Serum 25(OH)D deficiency is prevalent in HIV-negative CM patients and linked to neuroinflammation and increased risk of PIIRS. Serum 25(OH)D levels may serve as a useful prognostic marker, although further research is needed.

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PubMed2026

Retinal microstructural alterations as early phenotypes of depression in radiogenomics analysis.

BACKGROUND: With the increasing prevalence of depression, there is an urgent clinical need for early screening in depression. The retina offers a promising window for early screening in depression due to its rapid, non-invasive, objective, eye-brain correlated characteristics, but previous research has yielded conflicting alterations in retinal microstructure in depression. METHODS: We screened retinal optical coherence tomography and brain magnetic resonance imaging data in the UK Biobank to enroll 23,225 participants for retinal study of depression occurrence, and 1475 participants for the eye-brain association study. We also used genetic data (ID: ebi-a-GCST90014267 and ukb-d-20,448) from the Integrative Epidemiology Unit Open Genome-Wide Association Study for Mendelian randomization analysis. We used Cox regression to assess the association between retinal microstructure and depression risk, Mendelian randomization to infer causality, and mediation analysis to explore retina-brain pathway association. RESULTS: The Cox regression analysis showed that retinal ganglion cell-inner plexiform layer (GCIPL) thickness remained a significant predictor of depression. The Mendelian randomization analysis indicated a positive statistical association between GCIPL thickness and depression. Moreover, there was a significant positive correlation (all p < 0.001) between the volume of specific depression-related brain regions and the GCIPL thickness. Adjusting for age, sex, and head size, the mediation analysis provided preliminary evidence for a potential anatomical pathway linking retinal GCIPL thickness to depression-related brain regions through primary visual cortex and secondary visual cortex volumes. CONCLUSION: Thickened retinal GCIPL is a potential early phenotype of depression and has a potential association pathway with depression-related brain regions using a radiogenomics approach.

Humans↗