Tag: pubmed

J Orthop Surg (Hong Kong). 2025 Sep-Dec;33(3):10225536251407382. doi: 10.1177/10225536251407382. Epub 2025 Dec 7.

ABSTRACT

BackgroundThis study aims to compare the performance of two artificial intelligence (AI) models, ChatGPT-4.0 and DeepSeek-R1, in addressing clinical questions related to degenerative lumbar spinal stenosis (DLSS) using the North American Spine Society (NASS) guidelines as the benchmark.Methods15 clinical questions spanning five domains (diagnostic criteria, non-surgical management, surgical indications, perioperative care, and emerging controversies) were designed based on the 2013 NASS evidence-based clinical guidelines for the diagnosis and management of DLSS. Responses from both models were independently evaluated by two board-certified spine surgeons across four metrics: accuracy, completeness, supplementality, and misinformation. Inter-rater reliability was assessed using Cohen’s κ coefficient, while Mann-Whitney U and Chi-square tests were employed to analyze statistical differences between models.ResultsDeepSeek-R1 demonstrated superior performance over ChatGPT-4.0 in accuracy (median score: 3 vs 2, P = 0.009), completeness (2 vs 1, P = 0.010), and supplementality (2 vs 1, P = 0.018). Both models exhibited comparable performance in avoiding misinformation (P = 0.671). DeepSeek-R1 achieved higher inter-rater agreement in accuracy (κ = 0.727 vs 0.615), whereas ChatGPT-4.0 showed stronger consistency in ssupplementality (κ = 0.792 vs 0.762).ConclusionsWhile both AI models demonstrate potential for clinical decision support, DeepSeek-R1 aligns more closely with NASS guidelines. ChatGPT-4.0 excels in providing supplementary insights but exhibits variability in accuracy. These findings underscore the need for domain-specific optimization of AI models to enhance reliability in medical applications.

PMID:41353581 | DOI:10.1177/10225536251407382

Med Sci Monit. 2025 Dec 7;31:e949115. doi: 10.12659/MSM.949115.

ABSTRACT

BACKGROUND Peripheral intravenous (IV) access is a fundamental pre-hospital procedure performed by emergency medical services (EMS) personnel and remains the primary route for drug administration. Pediatric IV cannulation is often challenging in out-of-hospital settings. The aim of this study was to evaluate the frequency of peripheral intravenous access being established in pre-hospital settings by EMS staff in pediatric patients. MATERIAL AND METHODS This retrospective study analyzed 6331 records of emergency medical services (EMS) dispatches involving patients under 18 years of age between 2020 and 2022. The study protocol included an assessment of cannulation rate depending on the patient’s age, case characteristics, ICD 10 (International Classification of Diseases, Tenth Revision) diagnosis and whether the patient required transport to a hospital. RESULTS Peripheral intravenous access was established in 1073 of 6331 pediatric patients (16.94%). The cannulation rate increased significantly with age, from 1.03% in infants (<1 year) to 75.12% in adolescents (12-18 years) (p<0.001). Logistic regression analysis identified age, trauma (OR=1.96), poisoning (OR=3.88), and transfer by Helicopter Emergency Medical Services (HEMS) (OR=5.86) as predictors of IV cannulation (p<0.001). CONCLUSIONS The overall rate of peripheral intravenous access establishment in pediatric patients in pre-hospital settings is relatively low, with the lowest rates observed in children under 1 year of age. Age, trauma, poisoning, and referral to HEMS teams significantly increased the likelihood of cannulation. It is essential to develop evidence-based algorithms and targeted training to support EMS personnel in managing vascular access in critically ill children.

PMID:41353559 | DOI:10.12659/MSM.949115

Eur J Med Res. 2025 Dec 6;30(1):1215. doi: 10.1186/s40001-025-03512-4.

ABSTRACT

BACKGROUND: Age-associated memory impairment (AAMI) is a predementia state linked to endothelial dysfunction. The endothelial activation and stress index (EASIX) quantifies endothelial injury, yet its association with cognitive function remains unvalidated in population studies. This study aimed to evaluate the relationship between EASIX and cognitive performance.

METHODS: Data from adults aged ≥ 60 years in the NHANES 2011-2014 were analyzed. Multiple linear regression assessed associations between EASIX and cognitive function scores. LASSO regression selected variables, and six machine learning models (e.g., Random Forest and XGBoost) were developed. SHAP values interpreted feature importance.

RESULTS: Among 2,763 participants, EASIX showed a significant negative correlation with all cognitive scores (P < 0.05). The ElasticNet model outperformed other models. SHAP analysis identified EASIX as one of the top four influential variables, with cognitive function levels demonstrating a declining trend as EASIX score increased, particularly among older adults.

CONCLUSIONS: EASIX is significantly negatively associated with cognitive function, especially in advanced age. Although the cross-sectional design precludes causal inference, it shows promise as a blood-based biomarker for early screening and risk assessment of cognitive decline, supporting its potential clinical utility.

PMID:41353552 | DOI:10.1186/s40001-025-03512-4

J Cardiothorac Surg. 2025 Dec 6;20(1):452. doi: 10.1186/s13019-025-03718-x.

ABSTRACT

BACKGROUND: Outcomes of aortic root replacement (ARR) versus conservative root approach (CRA) in patients undergoing acute type A aortic dissection (ATAAD) repair remained controversy.

METHODS: The present study was a pooled meta-analysis of Kaplan-Meier-derived individual patient data (IPD) from comparative studies published by September 29, 2024.

RESULTS: Forty studies met our eligibility criteria, comprising 11,734 patients (4212 in the ARR group and 7522 in the CRA group). In the overall population, the overall survival was similar between the ARR and CRA groups (hazard ratio [HR], 0.95; 95% CI, 0.87-1.02; p = 0.17, log-rank test p = 0.47), while ARR was associated with lower risk of reoperation compared with CRA (HR 0.72; 95% CI, 0.59-0.87; p < 0.001, log-rank test p < 0.001). Subgroup analysis revealed that valve-sparing root replacement (VSRR) was associated with better overall survival compared with CRA (HR 0.74; 95% CI, 0.60-0.91; p = 0.004, log-rank test p = 0.003), while Bentall procedure was not (HR 1.06; 95% CI, 0.93-1.20; p = 0.37, log-rank test p = 0.39). The restricted mean survival time (RMST) was overall 12.9 months longer with VSRR compared with CRA (p = 0.009). The meta-regression analyses did not find statistically significant coefficients for the covariates of age, male sex, hypertension, diabetes, Marfan syndrome, bicuspid aortic valve, aortic root diameter and ascending aorta diameter in the CRA arm.

CONCLUSIONS: In patients underwent ATAAD repair, the overall survival was comparable between ARR and CRA, while ARR was associated with lower risk of reoperation compared with CRA. VSRR was associated with better long-term survival compared with CRA, while Bentall procedure was not.

PMID:41353548 | DOI:10.1186/s13019-025-03718-x

Eur J Med Res. 2025 Dec 6. doi: 10.1186/s40001-025-03609-w. Online ahead of print.

ABSTRACT

OBJECTIVE: To analyze the correlation between paraspinal muscle atrophy, facet joint degeneration, and degenerative scoliosis (DS).

METHODS: A retrospective study included 231 chronic low back pain patients from Zhongda Hospital Affiliated to Southeast University (January 2023-January 2024). Radiographic diagnosis assigned 150 patients to DS group (subclassified into mild [n = 72], moderate [n = 56], severe [n = 22]) and 81 to non-DS control group. Using T2-weighted MRI at L3-S1 levels, ImageJ software measured multifidus (MF) and erector spinae (ES) cross-sectional area (CSA) and functional muscle ratio (LCSA/GCSA). Surgimap software quantified facet joint angle (FJA), facet overhang (FO) length, and facet joint space width (FJSW). Logistic regression analyzed risk factors with ROC curves determining diagnostic thresholds.

RESULTS: The non-DS group demonstrated a significantly higher proportion of males (P = 0.023) and greater bone mineral density (P = 0.043) compared to the DS group. Regarding paraspinal muscle parameters, the non-DS group exhibited significantly larger MF CSA, MF + ES CSA, and LCSA/GCSA at the L3/4, L4/5, and L5/S1 levels, as well as a larger ES CSA at the L3/4 level (all P < 0.05). Conversely, the ES CSA at the L5/S1 level was significantly smaller in the non-DS group. For facet joint parameters, the non-DS group showed significantly smaller FJA, FO Length at the L3/4, L4/5, and L5/S1 levels, and smaller FJSW at the L3/4 and L4/5 levels (all P < 0.05). Within the DS group, significant differences were observed between the convex and concave sides at all L3-S1 levels for LCSA/GCSA, MF CSA, ES CSA, FJA, FO Length, and FJSW (all P < 0.05). With increasing severity of DS, there was a progressive decrease in LCSA/GCSA, MF CSA, and ES CSA, and a progressive increase in FJA and FO Length across the L3-S1 levels (all P < 0.01). Post-hoc analysis revealed significant differences in the majority of muscle parameters between severe DS and mild/moderate DS (P < 0.05). Correlation analysis indicated that, except for FJSW at L5-S1 (P = 0.526), the Cobb angle was negatively correlated with MF CSA, ES CSA, LCSA/GCSA, and FJSW, and positively correlated with FJA and FO Length (all P < 0.001). In both the DS and non-DS groups, most LCSA/GCSA and other CSA measurements demonstrated no significant correlations with FJA, FO length, and FJSW. Among the few statistically significant correlations observed, all were weak (rho < 0.30). Multivariate logistic regression analysis identified the following risk-associated factors for DS: lower BMD (OR = 0.802, P = 0.032), reduced LCSA/GCSA (OR = 0.005, P = 0.003), smaller MF CSA (OR = 0.969, P = 0.027), smaller ES CSA (OR = 0.973, P = 0.014), larger FJA (OR = 1.075, P = 0.016), and greater FO length (OR = 1.067, P = 0.001). ROC analysis yielded AUCs/cut-offs: BMD (0.581/- 0.900 T-score), LCSA/GCSA (0.712/0.805), MF CSA (0.608/635 mm²), ES CSA (0.463/832 mm²), FJA (0.627/57°), FO length (0.651/6.550 mm).

CONCLUSION: DS patients demonstrate progressive paraspinal muscle atrophy, sagittal-oriented facet joints, and advanced facet degeneration correlating with scoliosis severity. Diagnostic thresholds indicating DS probability are BMD < – 0.900 T-score, LCSA/GCSA < 0.805, MF CSA < 635 mm², ES CSA < 832 mm², FJA > 57°, and FO length > 6.550 mm.

PMID:41353546 | DOI:10.1186/s40001-025-03609-w

Thromb J. 2025 Dec 6. doi: 10.1186/s12959-025-00799-5. Online ahead of print.

NO ABSTRACT

PMID:41353544 | DOI:10.1186/s12959-025-00799-5

BMC Nephrol. 2025 Dec 6. doi: 10.1186/s12882-025-04669-0. Online ahead of print.

ABSTRACT

BACKGROUND: Acute kidney injury (AKI) is a prevalent and severe complication following non-cardiac surgery, often leading to poor outcomes. Despite the critical role of inflammation in AKI pathogenesis, reliable preoperative predictive models remain elusive. The pan-immune inflammation value (PIV), a novel index that integrates counts of neutrophils, platelets, lymphocytes, and monocytes, provides a comprehensive reflection of systemic inflammation. This study aimed to develop and validate a clinical prediction model for postoperative AKI (PO-AKI) in non-cardiac surgical patients.

METHODS: This retrospective study included adult patients who underwent non-cardiac surgery under general anaesthesia. The objective was to construct a model to predict PO-AKI. The statistical analysis focused on model construction and validation. LASSO regression was employed for variable selection to identify the most parsimonious set of predictors. The model’s performance was evaluated based on its discriminative ability (AUC), with calibration and decision curve analysis used to assess its clinical utility.

RESULTS: The cohort consisted of 1,164 adult patients. AKI was diagnosed in 8.4% of patients. The primary outcome, the performance of the prediction model, showed an AUC of 0.70. The model incorporated PIV and emergency surgery. The secondary outcome, the discriminative ability of PIV alone, yielded an AUC of 0.691. The model demonstrated good calibration and provided a clinical net benefit across a wide range of threshold probabilities.

CONCLUSION: We developed and validated a prediction model for PO-AKI. This model, which integrates PIV and emergency surgery, serves as an effective tool for preoperative risk stratification, facilitating the identification of high-risk patients and optimizing perioperative management.

PMID:41353543 | DOI:10.1186/s12882-025-04669-0

BMC Public Health. 2025 Dec 7. doi: 10.1186/s12889-025-25844-w. Online ahead of print.

ABSTRACT

BACKGROUND: Latent tuberculosis infection (LTBI) is a significant reservoir for active tuberculosis development. Identifying key risk factors is crucial for prevention strategies. Machine learning techniques can uncover complex relationships between risk factors and disease outcomes.

METHODS: Data were collected from China’s Tuberculosis Management Information System. LTBI was defined by positive tuberculin skin tests. A case-control design comparing LTBI (n = 669) with active tuberculosis (ATB, n = 669) patients was employed. Propensity score matching (1:1) was performed using age, gender, and education level. Four machine learning models (random forest, XGBoost, support vector machine, and neural network) were developed for feature importance analysis. Least Absolute Shrinkage and Selection Operator (LASSO) regression and logistic regression identified key risk factors. Bootstrap resampling (n = 1,000 iterations) assessed model stability with 95% confidence intervals. Shapley Additive Explanations (SHAP) analysis provided feature importance interpretation. A risk nomogram was constructed and evaluated using receiver operating characteristic curves, calibration plots, and decision curve analysis.

RESULTS: Among 1,338 matched participants, XGBoost demonstrated superior performance (AUC = 0.898, accuracy = 85.7%, sensitivity = 84.2%, specificity = 86.9%). SHAP analysis revealed age group (mean |SHAP value|=0.818) as the most influential predictor, followed by medical insurance type (0.599), income group (0.523), and education level (0.439). Logistic regression identified 11 significant risk factors: age (OR = 2.35, 95%CI: 1.86-2.96), BMI (OR = 0.81, 95%CI: 0.71-0.93), smoking status, occupational dust exposure, diabetes, medical insurance type, immunosuppressant use, education level, silicosis, anemia, and TB contact history. The nomogram showed good discrimination (AUC = 0.839) and clinical utility, identifying 64.44% of subjects as high-risk with 53.62% confirmed as true positives at 20% risk threshold.

CONCLUSION: This study successfully identified key LTBI risk factors using machine learning approaches. The developed nomogram provides a practical tool for targeted screening in resource-limited settings. Interventions targeting modifiable factors such as smoking cessation and occupational dust control may reduce LTBI and active TB burden.

PMID:41353539 | DOI:10.1186/s12889-025-25844-w

BMC Med Educ. 2025 Dec 6. doi: 10.1186/s12909-025-08336-8. Online ahead of print.

ABSTRACT

BACKGROUND: The rapid integration of blended learning (BL) into anatomy education has transformed traditional teaching. the preferences and perceptions of medical students toward BL, and its impact on anatomy learning, remain underexplored.

OBJECTIVE: This multi-institutional study aimed to assess medical students’ preferences and perceptions regarding BL in anatomy education, and to identify factors influencing their anatomy learning across three universities in Egypt and Oman.

METHODS: A comparative cross-sectional survey was conducted among 615 medical students from Alexandria University (Egypt), Mansoura University (Egypt), and National University (Oman). The validated Blended Learning Questionnaire (BLQ), adapted from Western Sydney University, was administered online. The BLQ evaluated preferences for learning modalities, satisfaction with BL, the role of self-regulated learning (SRL), small group activities. Data were analysed using descriptive statistics, chi-square tests, t-tests, and ANOVA, with significance set at p < 0.05.

RESULTS: students expressed a preference for BL and online modalities over traditional face-to-face lectures, with the highest preference for BL observed in National University. Female students favoured small group learning, while SRL was most valued by National students. The use of audio-visual resources and flexibility in accessing online materials were highly rated. Institutional differences were noted in preferred online tools and the value of small group activities.

CONCLUSION: Medical students across diverse settings prefer blended and online learning approaches for anatomy, highlighting the need for flexible, resource-rich, and student-cantered curricula. These highlight the importance of supporting SRL and using technology to optimize anatomy education, with implications for curriculum design and faculty development.

PMID:41353536 | DOI:10.1186/s12909-025-08336-8

BMC Med Imaging. 2025 Dec 6. doi: 10.1186/s12880-025-02094-1. Online ahead of print.

ABSTRACT

BACKGROUND: This study explores the feasibility and effectiveness of an interpretable machine learning model for assessing the pathological grading of pancreatic ductal adenocarcinoma (PDAC) using radiomics and topological features derived from contrast-enhanced CT habitat subregions.

METHODS: A retrospective study was conducted on a total of 306 patients with PDAC from two hospitals: a training cohort (n = 176), a validation cohort (n = 76), and a test cohort (n = 54). K-means clustering analysis was first used to segment portal venous phase CT images into three habitat regions. Radiomics features of the whole-tumour region, along with radiomics and topological features of each habitat region, were extracted respectively. LASSO regression was applied for feature dimensionality reduction to construct the radiomics score (Rad-score) for the whole-tumour region and the habitat score (H-score) for each habitat region. Meanwhile, logistic regression was used to identify statistically significant predictors from clinical and semantic features. Five machine learning algorithms were used to construct Habitat-TDA models, with interpretability analysis performed via SHAP analysis.

RESULTS: Total volume, diabetes, and M staging were identified as independent risk factors for predicting the pathological grading of PDAC, and were used to construct the Clinical model. 6 radiomics features with non-zero coefficients were selected to calculate the Rad-score, which was further used to construct the WholeRad model. In the three habitat regions, 6, 5, and 6 topological and radiomics features were included to generate the H-score. The logistic regression algorithm performed best in the validation and test cohorts and was ultimately selected as the classifier for constructing the Habitat-TDA model. SHAP analysis showed that H-score1, derived from Habitat Region 1 (the habitat region with the lowest average CT value), has the most significant average impact on the model output intensity. The AUC values of the Habitat-TDA model in the training, validation, and test cohorts were 0.894, 0.872, and 0.829, all outperforming the clinical model (0.784, 0.765, 0.731) and WholeRad model (0.817, 0.810, 0.773).

CONCLUSIONS: The Habitat-TDA model improves the accuracy and interpretability of preoperative predictions of PDAC grading, providing a promising tool for personalised management.

PMID:41353533 | DOI:10.1186/s12880-025-02094-1