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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.

Humans↗
PubMed2026

Structured Visualization for Laparoscopic Skill Acquisition:A Randomized Controlled Study.

OBJECTIVE: To evaluate whether structured visualization can support acquisition of basic laparoscopic skills during simulation training and whether this approach can achieve outcomes comparable to repeated physical practice. DESIGN: Prospective randomized comparative study. SETTING: Simulation-based laparoscopic skills training at a university-affiliated teaching center. PARTICIPANTS: Fifty laparoscopy-naive medical students were randomly assigned to a laparoscopic practice group or a visualization group. The laparoscopic practice group performed a validated Gynecological Endoscopic Surgical Education and Assessment (GESEA) Laparoscopic Skills Training and Testing (LASTT) hand-eye coordination task 7 consecutive times. The visualization group performed the same task physically on attempts 1, 4, and 7, while attempts 2, 3, 5, and 6 consisted of guided visualization. Each attempt lasted up to 2 minutes, and performance was scored as the number of correctly placed rings (range 0-12). RESULTS: Baseline performance was comparable between groups (2.92&#x202f;&#xb1;&#x202f;2.14&#x202f;vs 2.88&#x202f;&#xb1;&#x202f;1.72; p&#x202f;=&#x202f;0.94). Both groups improved significantly over time (p&#x202f;<&#x202f;0.001). No statistically significant between-group differences were found on the 4th attempt (6.52&#x202f;&#xb1;&#x202f;3.25&#x202f;vs 5.76&#x202f;&#xb1;&#x202f;2.57; p&#x202f;=&#x202f;0.48) or 7th attempt (8.24&#x202f;&#xb1;&#x202f;2.86&#x202f;vs 7.44&#x202f;&#xb1;&#x202f;2.99; p&#x202f;=&#x202f;0.39). The final physical performance of the visualization group was significantly better than the 3rd physical attempt of the laparoscopic practice group (p&#x202f;=&#x202f;0.025). CONCLUSIONS: Structured visualization may support early laparoscopic skill acquisition and achieve short-term outcomes comparable to repeated hands-on simulator training. Visualization should be considered an adjunct, rather than a replacement, for physical practice in simulation-based laparoscopic education.

Laparoscopy↗
PubMed2026

Assessing perinatal depression identifying abilities among maternal and child health workers in rural China using smartphone-based virtual patients: a multi-center cross-sectional study.

OBJECTIVE: To assess rural maternal and child health (MCH) workers' virtual patients (VPs)-assessed performance in identifying perinatal depression (PND) using smartphone-based VPs, and to identify factors associated with this performance in rural Hunan, China. METHODS: A multicentre cross-sectional study was conducted in Hunan Province, China. A standardized questionnaire collected demographic and work-related characteristics of rural MCH workers. Smartphone-based VPs were used to assess PND identification performance in a simulated clinical scenario. An overall score &#x2265;60 was used as a prespecified operational benchmark across consultation, ancillary assessment, diagnosis, management, and health education domains. Data were analyzed using SPSS 26.0. RESULTS: A total of 375 rural MCH workers participated, yielding an effective response rate of 90.4%. Only 25.9% met the prespecified operational benchmark for VP-assessed PND identification performance. The mean accuracy scores for consultation, ancillary assessment, diagnosis, management, and health education were 94%, 48%, 64%, 58%, and 74%, respectively. Complete consultation accuracy was higher among MCH workers from township health centers than among those from county-level MCH hospitals. MCH workers aged 18-39 years showed higher odds of complete diagnostic accuracy for PND than those aged &#x2265;40 years. CONCLUSIONS: Smartphone-based VP assessment was feasible in rural MCH settings and revealed suboptimal PND identification performance. Mobile VPs may help identify frontline performance gaps and inform targeted training, but further validation against real-world clinical performance, or standardized patient encounters is needed before large-scale implementation. These findings may support targeted capacity-building for rural MCH workers and more equitable perinatal mental health care.

Humans↗
PubMed2026

Efficacy of the NMIC-150 system in identifying extended-spectrum beta-lactamases in clinical isolates.

Extended-spectrum beta-lactamases (ESBLs) are significant contributors to the growing global crisis of antimicrobial resistance. This study evaluated the performance of the NMIC-150 System for susceptibility testing of third-generation cephalosporins (3GCs) and assessed whether ceftazidime-avibactam and aztreonam-avibactam could identify ESBL-producing carbapenem-resistant Enterobacterales (CREs). A total of 278 non-duplicate clinical isolates (Klebsiella pneumoniae, E. coli, and Proteus mirabilis) were analyzed. Antimicrobial susceptibility was determined using reference broth microdilution (BMD) and the NMIC-150 System. ESBL production was defined as an &#x2265;eight-fold reduction in the minimum inhibitory concentration (MIC) of 3GCs in the presence of clavulanic acid, according to CLSI criteria. Whole-genome sequencing was performed to characterize ESBL and carbapenemase genes among 3GC-resistant isolates. A Random Forest model was used to predict ESBL-producing isolates based on MIC values. The NMIC-150 System demonstrated over 90% categorical and essential agreement with BMD for ceftazidime and ceftriaxone, along with robust predictive performance via Random Forest analysis. These findings suggest that the NMIC-150 System is a reliable platform for 3GC susceptibility testing and that an &#x2265;eight-fold MIC reduction with ceftazidime-avibactam or aztreonam-avibactam may serve as a phenotypic indicator of ESBL production in CRE isolates. In conclusion, the NMIC-150 System shows potential for routine antimicrobial resistance surveillance and may facilitate the rapid identification of ESBL-producing CREs in clinical settings.

Microbial Sensitivity Tests↗
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&#xa0;=&#xa0;549) and a validation set (n&#xa0;=&#xa0;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&#xa0;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&#xa0;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.

Humans↗
PubMed2026

Artificial intelligence (AI) uses in stereotactic radiosurgery (SRS): diagnosis with brain metastasis (BM) - A systematic review.

BACKGROUND: Brain metastases (BM) are the most common intracranial tumors in adults, and stereotactic radiosurgery (SRS) has become a mainstay of management. However, several diagnostic challenges persist in the SRS pathway, particularly the differentiation of radiation necrosis (RN) from true tumor progression, which conventional MRI and even advanced imaging techniques often cannot reliably resolve. Recent advances in artificial intelligence (AI) offer the potential to address these diagnostic limitations. This systematic review synthesizes current literature on AI applications for MRI-based diagnostic decision support in BM patients undergoing SRS, with a focus on radiomics and deep learning tools for distinguishing RN from progression, classifying molecular and histologic subtypes, and predicting treatment response. METHODS: A systematic review was performed in accordance with PRISMA guidelines. PubMed, Web of Science, and Scopus were searched using a targeted query combining terms related to AI, brain metastasis, diagnosis or imaging, and SRS. After screening 483 records and applying strict inclusion and exclusion criteria, 18 studies published between 2015 and 2025 were included. Data were extracted on study design, cohort characteristics, imaging modality, AI methodology, validation strategy, and reported diagnostic performance. RESULTS: Among the 18 included studies, AI models demonstrated strong performance across diagnostic tasks in the BM-SRS pathway. The differentiation of RN from true tumor progression was the most extensively studied application, addressed by 14 of 18 studies, with reported AUCs ranging from 0.71 to 0.94. Support vector machines, random-forest ensembles, convolutional neural networks, and transformer-based multimodal architectures were widely used. The literature evolved from single-sequence radiomic classifiers in 2018 to multimodal deep learning frameworks fusing imaging with clinical and genomic data in 2025. Contrast-enhanced T1-weighted MRI was the dominant imaging input, and texture-based radiomic features (GLCM, GLSZM, GLDM, and wavelet-derived features) were the most consistently predictive. The highest-performing models reached AUCs of 0.85-0.91 through multimodal integration of imaging with clinical and genomic features, and consistently outperformed expert neuroradiologist read on matched cases. Remaining studies addressed longitudinal segmentation-based detection of local failure and adverse radiation effects, BRAF mutation status in melanoma BM, early Gamma Knife treatment response, and primary tumor histology classification, with more variable performance. CONCLUSION: AI models, particularly those integrating MRI-derived radiomic features with clinical and genomic data, show high accuracy in supporting diagnostic decisions for BM patients treated with SRS. The post-SRS differentiation of radiation necrosis from true tumor progression has reached the greatest level of maturity and is closest to clinical translation, with potential to reduce unnecessary biopsies, personalize surveillance intervals, and rationalize treatment-pathway decisions. Other diagnostic applications, including molecular subtyping and primary tumor histology classification, remain exploratory and require further multicenter validation. Integration of AI tools into multidisciplinary tumor-board workflows, combined with prospective validation and standardized reporting, will be essential to realize the full clinical benefits of AI in SRS for brain metastases.

Humans↗
PubMed2026

Muscle Massage Adding Capacitive Resistive Electric Transfer Therapy in Active or Sham Condition for Post-Exercise Recovery in Athletes: A Crossover Clinical Trial.

The increasing demands of elite sports reduce recovery time, impair performance, and increase injury risk. Efficient lactate transport is essential for postexercise recovery. Capacitive resistive electric transfer (CRET) therapy enhances deep tissue heating, induces vasodilation, and promotes circulation. To evaluate whether adding active CRET to a standardized muscle recovery massage, compared with the same massage plus sham CRET, influences indicators of muscle recovery following a maximal anaerobic effort test. A randomized, single-blind, sham-controlled, and crossover clinical trial was conducted in 25 athletes. Participants completed four visits and, after the maximal power and anaerobic capacity test (Wingate test), received a standardized muscle recovery massage combined with either active CRET or sham CRET. Blood lactate levels, muscle oxygenation, muscle thickness, echogenicity, knee extension force, and muscle activity were assessed before and after the test, after treatment, and 24&#xa0;hours later. Compared with massage plus sham CRET, massage plus active CRET was associated with lower blood lactate concentration at 60&#xa0;min postexercise (p&#xa0;=&#xa0;0.029). Ultrasound-derived muscle thickness and echogenicity also differed between conditions at several time points (p&#xa0;<&#xa0;0.05). However, no significant differences were observed in Wingate test performance, force, muscle activity, and oxygenation between conditions. In athletes performing repeated Wingate exercise, adding active CRET to massage was associated with lower blood lactate concentration at 60&#xa0;min postexercise and with differences in ultrasound-derived muscle thickness and echogenicity compared with sham CRET plus massage. However, these between-condition differences were not accompanied by clear short-term functional recovery benefits. TRIAL REGISTRATION: NCT06906146.

Humans↗
PubMed2026

Comparative evaluation of molecular technologies for the identification of prevalent non-tuberculous mycobacteria in pulmonary infections: a systematic review and meta-analysis.

BACKGROUND: The increasing prevalence of non-tuberculous mycobacteria pulmonary disease (NTM PD) is a burden to public health. Successful management of NTM PD critically depends on accurate species identification and reliable drug susceptibility testing to guide appropriate antibiotic therapy. Emerging molecular technologies offer rapid diagnostic solutions compared to conventional methods, but their performance varies. This study aims to provide a comprehensive evaluation of current molecular techniques for NTM identification and to present a global antibiotic resistance profile. METHODS: A systematic literature search was conducted in PubMed and Web of Science for studies published between 2005 and 2024. Studies applying molecular methods for NTM identification and resistance detection in humans were included. Data on study characteristics, diagnostic methods, sample types, sample sizes, identification sensitivity, and drug susceptibility results were extracted. Meta-analysis was performed using R with the meta4diag package. The quality of included studies was assessed using the QUADAS-2 tool. RESULTS: The analysis included 49 studies on NTM identification and 33 studies on antibiotic resistance. For species identification, all evaluated molecular technologies (MALDI-TOF MS, PCR-based methods, Sequencing, DNA chip, and DNA strip) demonstrated high pooled sensitivities (>0.92). Subgroup analysis revealed that sample type significantly affected performance for MALDI-TOF MS. Preliminary analysis of antibiotic resistance rates revealed varying patterns. For slowly growing mycobacteria, a significantly high Ethambutol resistance rate was observed in M. avium (69.20%). Among rapidly growing mycobacteria, resistance to Imipenem was notable (54.22%), and Clarithromycin resistance varied significantly within the Mycobacterium abscessus complex. CONCLUSION: Emerging molecular technologies have revolutionized the methodology for NTM identification with excellent performance. However, their performance can be influenced by sample type, particularly for MALDI-TOF MS. The alarming and heterogeneous antibiotic resistance patterns also highlight the critical need for rapid and accurate species identification and drug susceptibility testing to inform effective therapeutic strategies. Key messagesMolecular technologies demonstrate high accuracy for NTM identification.Antibiotic resistance is a serious concern with variations among NTM species and subspecies.Rapid and accurate species identification and drug susceptibility testing are crucial for guiding effective clinical management of NTM PD.

Humans↗
PubMed2026

Systematic Review on the Effectiveness of Photobiomodulation Therapy on Enhancing Musculoskeletal Rehabilitation Outcomes after Total Knee and Hip Arthroplasty.

BACKGROUND: Total knee and hip arthroplasty (TKA/THA) effectively alleviate chronic pain and improve joint function and quality of life. However, postoperative recovery is often hindered by pain, edema, restricted range of motion (ROM), and muscle weakness, necessitating structured physical rehabilitation. Photobiomodulation Therapy (PBMT) has been proposed as an adjunct modality to enhance rehabilitation outcomes. This systematic review evaluates the efficacy of PBMT in reducing postoperative pain and edema as well as improving ROM and functional performance compared with placebo or standard physical therapy in post-arthroplasty patients. METHODS: A systematic search of PubMed, ProQuest, ScienceDirect, and Google Scholar was conducted from inception to May 31, 2025. Only prospective controlled trials assessing PBMT effects on postoperative pain, swelling, ROM, or functional performance following TKA or THA were included. Case reports, uncontrolled studies, and trials focusing on unrelated outcomes were excluded. Article selection involved title, abstract, and full-text screening, with additional studies identified through reference lists. The review is registered in PROSPERO (CRD420251123169). RESULTS: Four randomized controlled trials (RCTs; n = 133 participants) met the inclusion criteria, three studies focused on TKA, and one study examined THA. PBMT significantly improved postoperative pain and edema reduction in three trials, while two studies demonstrated enhanced ROM and functional outcomes compared with control or placebo interventions. PEDro scores ranged from 8 to 10, indicating moderate-to-high methodological quality. CONCLUSIONS: PBMT shows promising short-term benefits in reducing postoperative pain and swelling and improving functional performance after knee or hip arthroplasty. However, due to heterogeneity in treatment parameters and limited high-quality trials, conclusive evidence remains insufficient. Future large-scale RCTs with standardized PBMT parameters and extended follow-up are warranted to confirm its clinical efficacy.

Humans↗
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.

Humans↗
PubMed2026

Dual-tasking reveals severity-dependent reorganization of cortical beta energy landscapes in Parkinson's disease.

Dual-task impairment is a hallmark of Parkinson's disease (PD), yet the large-scale neural mechanisms underlying postural-motor interference remain poorly understood. In particular, it is unclear how cortical network dynamics reorganize across disease severity when postural control competes with concurrent task demands. This study investigated EEG-derived beta-band cortical energy landscapes in healthy older adults, early-stage PD, and mid-stage PD during single- and dual-task conditions. Dual-task behavioral cost increased with disease severity for concurrent manual performance (p&#xa0;<&#xa0;0.001), whereas a quadratic pattern was observed for postural performance. Energy landscape analysis revealed severity-dependent reconfiguration of cortical beta dynamics. Dual-task-related landscape changes in effective network flexibility (&#x394;Neff), landscape geometry (&#x394;Evar and &#x394;Gmag), and dominant low-energy attractor organization (&#x394;Low mass and &#x394;Low area) showed significant monotonic trends (p&#xa0;<&#xa0;0.05), reflecting progressive constrained cortical network dynamics with advancing PD severity. In addition, dual-task-related landscape alterations were associated with clinical severity, as indexed by Hoehn and Yahr stage (|r|&#xa0;=&#xa0;0.353-0.423, p&#xa0;=&#xa0;0.016-0.048), and showed associations with motor impairment, as measured by MDS-UPDRS part III scores (|r|&#xa0;=&#xa0;0.333-0.455, p&#xa0;=&#xa0;0.009-0.063). These findings demonstrate that dual-task demands induce severity-dependent reconfiguration of cortical beta energy landscapes in PD. Energy landscape geometry may capture systems-level neural constraints associated with dual-task susceptibility in PD, providing a physiologically grounded framework to characterize disease-related functional vulnerability.

Humans↗
PubMed2026

A single session of high-definition transcranial direct current stimulation does not modulate effects of knee two-point discrimination training or sensorimotor function in healthy adults: Double-blind randomised controlled trial.

BACKGROUND: Benefits of sensory-training interventions on sensorimotor outcomes are inconsistent, and it is unclear whether transcranial direct current stimulation (tDCS) can enhance proprioception, which is fundamental to neuromuscular control. Existing evidence is dominated by upper-limb studies, so transferability to the knee is unclear. This study investigated whether a single session of anodal high-definition (HD)-tDCS, alone or combined with brief two-point discrimination (TPD) training, enhances knee sensorimotor function and performance in healthy adults. METHODS: In a double-blind randomised-controlled trial, 57 healthy participants received (1) 20-min, 1&#xa0;mA anodal HD-tDCS over the knee primary somatosensory (S1) map or sham stimulation, and (2) 15-min knee TPD training or no training. Knee somatosensory, sensorimotor, and functional performance measures were assessed pre- and immediately post-intervention. Three-way mixed-design ANOVAs, equivalence testing (smallest effect size of interest &#x3b7;2&#xa0;=&#xa0;0.02), Bayes factors, and linear mixed-effects models quantified effects. RESULTS: HD-tDCS, TPD training, and their sequential combination had no effect on any outcome measure (p&#xa0;&#x2265;&#xa0;0.05; &#x3b7;2&#xa0;&#x2264;&#xa0;0.015). Confidence intervals spanned equivalence bounds, and equivalence testing results were non-significant (p&#xa0;&#x2265;&#xa0;0.05). Bayes factors (<0.33) showed moderate evidence for the null (no effect). Mixed-effects modelling attributed &#x2264;8% of total variance to intervention fixed effects, with the remainder captured by participant-level random effects. CONCLUSION: A single 20-minute session of 1&#xa0;mA HD-tDCS, with or without brief TPD training, does not acutely modify knee somatosensory, sensorimotor, or functional performance in healthy adults. Existing evidence at the hand may not translate to the lower limb. Future work should investigate higher-dose, multi-session, task-concurrent, or network-targeted strategies in clinical populations.

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↗
PubMed2026

Distal versus proximal radial access for diagnostic cerebral angiography: comparative outcomes and learning curve analysis.

BACKGROUND AND PURPOSE: Distal transradial access (dTRA) is an alternative to proximal transradial access (pTRA) for neuroangiography, but comparative real-world data and evidence on its early learning curve remain limited. We compared procedural performance and access-site complications between dTRA and pTRA and evaluated the early learning curve of dTRA. METHODS: We retrospectively analyzed 470 diagnostic cerebral angiography procedures, representing 421 unique patients, performed via radial access at a single center between January 2025 and February 2026, including 237 dTRA and 233 pTRA procedures. Baseline characteristics, including age, sex, body mass index (BMI) category, aortic arch type, and antiplatelet/anticoagulant use, procedural performance, and clinically assessed access-site events were compared between groups. Radial artery occlusion (RAO) was assessed by postoperative bedside pulse examination and confirmed with Doppler ultrasound when clinical findings were uncertain. Multivariable logistic regression was used to evaluate predictors of RAO, persistent bleeding or repeated compression, hand edema, and a composite access-site event endpoint. Because repeated procedures occurred in a subset of patients and event counts were limited, first-procedure sensitivity analysis and analyses of infrequent outcomes were interpreted cautiously. The dTRA learning process was assessed in the first 100 dTRA cases performed by a single operator using multivariable regression, cumulative sum (CUSUM) analysis, segmented trend analysis, and phase-based comparisons. RESULTS: Baseline characteristics were comparable between groups, including age, male sex, BMI category, aortic arch type, and antiplatelet/anticoagulant use. Compared with pTRA, dTRA was associated with more puncture attempts (3.0 [2.0-4.0] vs 2.0 [1.0-3.0], P&#xa0;<&#xa0;0.001), longer puncture time (2.0 [1.0-5.0] vs 2.0 [1.0-3.0] min, P&#xa0;=&#xa0;0.003), lower first-pass success (19.4% vs 35.2%, P&#xa0;<&#xa0;0.001), and a higher crossover rate (11.4% vs 6.0%, P&#xa0;=&#xa0;0.037). However, dTRA was associated with a lower clinically assessed RAO rate (2.5% vs 7.7%, P&#xa0;=&#xa0;0.011). On multivariable analysis, pTRA was independently associated with higher odds of RAO (OR 3.27, 95% CI 1.26-8.49, P&#xa0;=&#xa0;0.015) and the composite access-site event endpoint (OR 3.12, 95% CI 1.55-6.28, P&#xa0;=&#xa0;0.001). Similar findings were observed in a sensitivity analysis restricted to the first procedure per patient. In the first 100 dTRA cases, cumulative dTRA experience was independently associated with shorter total procedure time (beta&#xa0;=&#xa0;-0.074&#xa0;min/case, P&#xa0;=&#xa0;0.009), while CUSUM and moving-average analyses suggested that the major learning effect occurred within approximately the first 10-15 cases. CONCLUSIONS: In this retrospective single-operator cohort, dTRA was associated with lower clinically assessed RAO than pTRA despite greater access difficulty. The early learning effect was mainly reflected in shorter total procedure time. These findings support the feasibility of dTRA but should be interpreted cautiously given the study's observational design and limited anatomical data.

Humans↗
PubMed2026

Influence of Immediate Post-Bleaching Polishing on Enamel Color, Morphology, and Sensitivity: A Randomized Clinical Trial.

OBJECTIVE: This study evaluated the influence of polishing on enamel color change after in-office bleaching treatment on tooth morphology and sensitivity. MATERIALS AND METHODS: A total of 50 volunteers were randomized into two groups (n&#x2009;=&#x2009;25): in-office bleaching with 35% hydrogen peroxide gel for 45&#x2009;min without polishing (GSEM) or with polishing (GP). Color analysis was performed four times, at the beginning of the starting line and immediately after the first, second, and third bleaching sessions with a spectrophotometer. A qualitative analysis of enamel morphology was performed under a scanning electron microscope. Tooth sensitivity was assessed daily using the Visual Analog Scale (&#x3b1;&#x2009;=&#x2009;0.05). RESULTS: There was no statistically significant difference (p&#x2009;>&#x2009;0.05) in tooth color change when comparing &#x394;E, &#x394;E00, and &#x394;WID between the groups. The enamel surface showed larger areas of irregularities and depressions in the GP group than in the GSEM group. There was no difference in tooth sensitivity (p&#x2009;>&#x2009;0.05) between the groups. CONCLUSIONS: Polishing after whitening in the office does not change the color and sensitivity of the tooth but promotes greater changes in the morphology of the enamel, such as increased surface roughness. CLINICAL RELEVANCE: Polishing immediately after teeth whitening causes greater changes in enamel surface morphology.

Humans↗
PubMed2026

The impact of artificial intelligence on critical thinking and clinical reasoning in health professions education: A systematic review and meta-analysis.

BACKGROUND: Critical thinking and clinical reasoning underpin healthcare professionals' ability to navigate uncertainties and deliver safe and effective care. With artificial intelligence (AI) advancement and growing adoption, AI-based educational tools are increasingly used to support these cognitive competencies' development. OBJECTIVE: To synthesize randomised and controlled clinical trials on AI-based educational tools in health professions education and examine their effects on critical thinking and clinical reasoning among health professions students. METHODS: Six electronic databases were searched from January 1, 2014 to July 28, 2025 was reviewed: PubMed, Cochrane Central Register of Controlled Trials, CINAHL, Scopus, Embase and Web of Science. Two independent reviewers performed data extraction and quality assessment using standardized JBI checklists. The GRADE approach was used to assess the certainty of evidence. Studies were pooled via random-effects meta-analyses or narrative syntheses. RESULTS: Fourteen randomised controlled trials and seven controlled clinical trials were included (n&#xa0;=&#xa0;21). Meta-analyses revealed small to medium effect sizes for the surrogate clinical reasoning outcomes of performance-based assessment scores (SMD 0.68; 95% CI [0.38, 0.98], p-value&#xa0;=&#xa0;0.00; I2&#xa0;=&#xa0;38%) and knowledge test scores (SMD 0.39; 95% CI [0.09, 0.69], p-value&#xa0;=&#xa0;0.01; I2&#xa0;=&#xa0;79%). Critical thinking and clinical reasoning skills and dispositions were narratively synthesized, with majority of included studies favouring AI-based interventions but the evidence had low to very low certainty. CONCLUSION: AI-based educational interventions may improve critical thinking and clinical reasoning among health profession students, but the evidence is very uncertain. This review offers preliminary insights but does not allow identification of optimal interventions or discipline-specific recommendations due to small sample sizes and substantial intervention heterogeneity. Further research is required to draw definitive conclusions. PROTOCOL REGISTRATION: CRD42025634074.

Humans↗
PubMed2026

Extended Long-Term Effects of Cognitive and Aerobic Training on Cognitive Function in Patients With Stress-Related Exhaustion Disorder: A 4.5-Year Follow-Up of a Randomized Controlled Trial.

Stress-related conditions like clinical burnout and exhaustion disorder (ED) are associated with enduring cognitive problems. We have previously demonstrated that the addition of computerised cognitive training (CCT) and aerobic training (AT) to a multimodal rehabilitation programme (MMR) yielded greater improvements in cognitive function compared to MMR alone in patients with ED. These effects were maintained at the 1-year follow-up for CCT, but not for AT. Building on these findings, the present study examined the extended long-term effects of CCT and AT on cognitive function, psychological health, and work ability, 4.5&#xa0;years after the interventions. Participants were recruited from a stress-rehabilitation clinic and 56 of the initial 132 participants returned for the 4.5-year follow-up. Assessments were conducted before (T1), immediately after (T2), 1-year after (T3) and 4.5-years after (T4) the interventions. Mixed model analyses assessed changes in the intervention groups relative to the control group from T1 to T4, with follow-up comparisons examining within-group stability of outcomes between T3 to T4. The primary outcome was cognitive performance on a global cognitive score. Secondary outcomes included domain-specific cognitive functioning, self-reported cognitive function, psychological health (burnout, depression, anxiety, and fatigue), and work ability. The analysis revealed sustained long-term effects on the global cognitive score, a trained updating task and episodic memory in the CCT group, with stable performance between one- and 4.5-year follow-up. The addition of AT did not yield any extended long-term effects on cognitive performance. Both groups showed extended long-term improvements in self-reported memory problems, although findings were mixed. Extended long-term improvements were observed on burnout for both groups, with additional effects on anxiety and work ability in the AT group. Notably, these effects were not present at the 1-year follow-up and are more plausibly explained by selective attrition rather than a delayed intervention effect. In conclusion, the result indicates that cognitive interventions such as CCT can have lasting positive effects on cognitive function in patients with ED, whereas the long-term psychological effects should be interpreted with caution. TRIAL REGISTRATION: ClinicalTrials.gov: NCT0073772.

Adult↗
PubMed2026

Can ChatGPT Replace Human Clinical Coders? A Comparative Study in Otology Billing.

OBJECTIVE: Evaluate the utility of the large language model (LLM), ChatGPT, for the analysis of operative notes and the generation of Current Procedural Terminology (CPT) codes in comparison to human clinical coders. STUDY DESIGN: CPT billing codes assigned by ChatGPT were compared to existing billing data. Otology practice within a tertiary academic center. METHODS: About 191 operative notes from a single surgeon (9/2022-10/2023) were analyzed. ChatGPT-3.5 and 4 models were prompted for CPT codes based on operative notes. Assessment included determining exact and partial match rates, sensitivity and specificity for targeted procedures, and work Relative Value Units (wRVU) differences between ChatGPT-generated and human-assigned codes. RESULTS: ChatGPT-3.5 achieved exact matches in 22% of cases and partial matches in 32%, while ChatGPT-4 achieved 14% exact and 33% partial matches. When cochlear implantation (CI) was excluded, performance dropped significantly. For CI, ChatGPT-3.5 demonstrated a sensitivity of 94% and specificity of 90%, while ChatGPT-4 showed a sensitivity of 96% and specificity of 92%. In contrast, performance on cartilage grafting was poor, with sensitivities of 4.2% for ChatGPT-3.5 and 0% for ChatGPT-4. ChatGPT-3.5 and 4 showed moderate CPT code matching accuracy among themselves, with slight agreement to human coders. Both models tended to underbill for wRVUs compared to human coders, with significant differences in the values generated. CONCLUSION: This study assessed ChatGPT's effectiveness in automating CPT code assignment for otologic surgeries. While the models achieved high sensitivity values for assigning codes related to cochlear implantation, both models struggled with complex cases, failed to apply modifiers, and often assigned fewer wRVUs. The findings highlight ChatGPT's potential in medical billing but indicate a need for further refinement.

Humans↗