Insurance Coverage for Endoscopic Bariatric Therapies in 2026: Analyses of Top Insurers in the US and Internal Validation.

Intratumor heterogeneity (ITH) impacts cancer progression, and its characterization is crucial. Clustering algorithms applied to the variant allele frequency (VAF) of mutations can facilitate the exploratory analysis of ITH. This study comparatively evaluated six clustering algorithms to characterize ITH by clustering mutations based on their VAFs. We utilized data from The Cancer Genome Atlas to analyze three cancer types by examining the distribution of clusters in the results from various methods and four internal validation metrics. The results indicated that the Density-Based Spatial Clustering of Applications with Noise (DBSCAN) and Variational Bayesian Gaussian Mixture Model methods identified an insufficient number of clusters in most tumor samples. The Hierarchical DBSCAN (HDBSCAN) and Ordering Points to Identify the Clustering Structure (OPTICS) algorithms exhibited greater variability in the number of clusters, while Affinity Propagation (AP) showed controlled behavior, and Mean-Shift demonstrated greater consistency. The Mean-Shift and AP methods were consistently superior in the validation metrics, in contrast to HDBSCAN and OPTICS, which had inferior performance. We conclude that Mean-Shift and AP are promising and accessible alternatives for the initial exploratory analysis of ITH by VAFs. A computational pipeline is provided on the Google Colab platform to facilitate future studies.

Development and internal validation of an explainable machine learning model for predicting textbook outcome after free flap reconstruction in oral cancer.

K-SALT Equation for Urinary Sodium Estimation in CKD.

A Stepwise Diagnostic Strategy Combining a Simplified US-Based Node-RADS with Postvascular Phase Perfluorobutane-CEUS for Indeterminate Cervical Lymph Nodes: A Dual-Center Retrospective Study.

From decision support to clinical integration: A scoping review of artificial intelligence in prehospital airway management.

Predictors of the Efficacy of Focused Ultrasound for Moderate-to-Severe Persistent Allergic Rhinitis: A Retrospective Cohort Study.

Accurate models for predicting pathological complete response (pCR) after neoadjuvant therapy (NAT) are increasingly needed in patients with human epidermal growth factor receptor 2 (HER2)-positive breast cancer (BC). In the present study, a nomogram was developed to estimate the probability of achieving a pCR in this population. Clinical data were retrospectively and prospectively collected from patients with HER2-positive BC at three time points: Before NAT, after the first cycle of neoadjuvant targeted therapy and after the completion of NAT before surgery. Logistic regression analysis was performed to identify independent predictors of pCR, and these variables were used to construct a predictive model and corresponding nomogram. Model performance was evaluated using calibration curves, decision curve analysis, receiver operating characteristic (ROC) curves and the area under the ROC curve (AUC), with retrospective data and prospective data used for internal and external validations, respectively. Logistic regression analysis of the retrospective cohort identified eight predictors associated with pCR, which were incorporated into a concise nomogram. Internal validation demonstrated good calibration and strong predictive performance, with an AUC value of 0.886 (P<0.001), sensitivity of 0.822 and specificity of 0.818. External validation further confirmed the excellent discriminatory ability of the model, yielding an AUC value of 0.961 (P<0.001), sensitivity of 1.000 and specificity was 0.875. Overall, this nomogram, which integrates multiple clinically relevant factors, may serve as a useful tool for predicting post-NAT pCR in patients with HER2-positive BC and may support more precise treatment decision-making in clinical practice.

The aims of this study were to compare baseline Impact of Weight on Quality of Life-Lite Clinical Trials Version (IWQOL-Lite-CT) scores between adults with obesity participating in either observational or clinical studies and to conduct a psychometric evaluation of the IWQOL-Lite-CT in the context of observational research. Analyses of covariance were used to compare baseline IWQOL-Lite-CT Total, Physical, Physical Function, and Psychosocial composite scores between participants with a body mass index (BMI) ≥ 30 in a US longitudinal survey (an observational study) and a multinational phase 3a study of subcutaneous semaglutide 2.4 mg for weight management (STEP 1). Data from the longitudinal survey were also used to evaluate the psychometric properties of the IWQOL-Lite-CT composite scores. Adjusting for BMI, age, and sex, on average, participants in the longitudinal survey scored lower than STEP 1 participants at baseline by 23.4 points on the IWQOL-Lite-CT Total, 20.7 on the Physical composite, 20.3 on the Physical Function composite, and 24.8 on the Psychosocial composite. While the internal consistency, test-retest reliability, and construct validity of the IWQOL-Lite-CT composite scores were confirmed in the longitudinal survey, responsiveness analyses were limited by small changes in weight. The weight-related functioning of US individuals with obesity who participated in a longitudinal survey was more limited than that of participants in the STEP 1 clinical trial. Psychometric results support use of the IWQOL-Lite-CT in observational research in addition to the clinical trial setting.

Transjugular intrahepatic portosystemic shunt (TIPS) has been widely adopted for the management of decompensated portal hypertension in cirrhotic patients. However, TIPS dysfunction remains a critical clinical challenge. This study aimed to identify risk factors for TIPS dysfunction and develop a predictive model to assess this complication. A retrospective study was conducted involving cirrhotic patients who underwent TIPS between 2017 and 2024. Among 1052 initially screened patients, 676 were finally included in the analysis. TIPS dysfunction was diagnosed using ultrasound and angiographic evaluations. The dataset was randomly divided into a training set and a validation set at a ratio of 7 : 3. A stepwise logistic regression analysis was performed in the training set to establish a predictive model. Ten-fold cross-validation was used for internal validation, and the validation set was applied for external validation. The predictive discrimination was evaluated using the receiver operating characteristic curve, and the calibration curve was used to assess the consistency. Shapley Additive Explanations analysis was conducted to interpret the model. Independent risk factors for TIPS dysfunction included age, main portal vein thrombosis, platelets, D value, angle β, and the angle between the selected hepatic vein and inferior vena cava. The model showed good predictive performance with an area under the curve of 0.896 (sensitivity 0.804, specificity 0.844, and accuracy 93.2%) for training set and 0.905 (sensitivity 0.870, specificity 0.826, and accuracy 86.5%) for validation set. The calibration curve from the 10-fold cross-validation in the training set also showed good agreement between prediction and observation. Shapley Additive Explanations analysis showed that Beta value is the most important feature affecting prediction, and allows the estimation of the specific risk of TIPS dysfunction for each individual in the model. This study developed a valid predictive model for assessing risk factors for TIPS dysfunction in cirrhotic patients. Recognition of these risk factors and optimized TIPS shunt management may contribute to improved outcomes in patients undergoing TIPS procedures.

A multi-omics analysis integrating mendelian randomization, brain functional connectivity, and transcriptomics to explore risk-associated features in non-small cell lung cancer.

Prediction and Clinical Application of Central Lymph Node Metastasis in Papillary Thyroid Carcinoma Based on Multi-modal Ultrasound Feature Fusion: A Multi-center Study.

An interpretable model combining brain imaging and clinical indicators for predicting overt hepatic encephalopathy.

ExSERA: The explainable machine learning model for skin sensitization risk assessment.

Development of treatment-specific outcome prediction models for intracranial aneurysm after coiling or clipping.

Data-driven clustering of chronic pain profiles using Swedish national registry data: Towards individualized decision support in interdisciplinary rehabilitation.

Drawing exclusively from randomized controlled trials (RCTs), the meta-analysis by Doheim et al. evaluates the comparative effectiveness of intravenous thrombolysis (IVT) vs nonthrombolytic standard of care (NT-SC) in patients with minor acute ischemic stroke (AIS). The authors identified 9 reports encompassing 13 RCTs. Multiple analytical approaches (Q and I2 measures, leave-out-one analyses, sensitivity analyses) were performed to address study heterogeneity. Findings show that, compared with NT-SC, IVT was not significantly associated with excellent functional recovery poststroke (odds ratio 0.85, 95% CI 0.70-1.03). IVT was associated with lower odds of achieving functional independence and higher odds of symptomatic intracranial hemorrhage and mortality at 90 days. A major strength is the study's exclusive inclusion of RCTs, which minimizes methodological heterogeneity and enhances internal validity. Limitations include (1) small number of included studies precluding formal assessment of publication bias; (2) exclusion of observational or quasiexperimental studies, potentially overlooking valuable real-world evidence; (3) variability in the definition of minor AIS; and (4) insensitivity of conventional outcome measures such as the modified Rankin Scale to capture nonmotor functional deficits such as fatigue and return-to-work capability, which are highly relevant to patients with minor stroke but remain underrepresented in traditional assessments. In conclusion, this meta-analysis suggests that IVT does not confer additional benefit over NT-SC for minor nondisabling stroke. Future studies would benefit from incorporating broader, patient-centered functional outcomes to better capture the subtleties of disability in this patient population.

Preoperative frailty for predicting in-hospital mortality in patients after cardiac surgery: an interpretable Machine learning model based on a retrospective multicenter cohort study.

Objective: To explore the risk factors for severe Chlamydia pneumoniae pneumonia (SCPP) in children and construct a predictive model. Methods: The retrospective cohort study included 179 children with Chlamydia pneumoniae pneumonia admitted to the Children's Hospital Affiliated to Zhengzhou University between January 2024 and June 2025. Their general information, clinical symptoms, and main laboratory indicators were collected. The included patients were randomly divided into a training cohort and an internal validation cohort at a ratio of 7:3, and were further categorized into SCPP and non-SCPP groups according to disease severity. In the training cohort, univariate analysis, Lasso regression and multivariate Logistic regression were used to screen the risk factors for SCPP in children, and a nomogram model was constructed. Model performance was validated in both the training and internal validation cohorts using receiver operating characteristic curve, calibration curve, Hosmer-Lemeshow test, decision curve analysis and clinical impact curve. Results: Of the 179 children with Chlamydia pneumoniae pneumonia, there were 106 males and 73 females, including 125 cases in the training cohort and 54 cases in the internal validation cohort. In the training cohort, there were 60 cases in the SCPP group (37 males and 23 females) and 65 cases in the non-SCPP group (39 males and 26 females). In the internal validation cohort, there were 25 cases in the SCPP group and 29 cases in the non-SCPP group. Multivariate Logistic regression analysis showed that co-infection (OR=2.66, 95%CI 1.04-6.79), fever duration (OR=1.94, 95%CI 1.07-3.54), erythrocyte sedimentation rate (OR=2.38, 95%CI 1.30-4.38), interleukin-8 (OR=2.04, 95%CI 1.17-3.55), lactate dehydrogenase (OR=1.84, 95%CI 1.06-3.19) were independent risk factors for SCPP in children (all P<0.05). The areas under the receiver operating characteristic curve were 0.88 (95%CI 0.82-0.94) and 0.83 (95%CI 0.73-0.94) in the training cohort and internal validation cohort, respectively. Calibration curve and Hosmer-Lemeshow test showed high consistency between model predictions and the actual situation (training cohort P=0.787; internal validation cohort P=0.680). Decision curve analysis revealed that the model yielded positive net benefit within the threshold probability range of 8%-90%. Clinical impact curve analysis confirmed that the model could accurately screen intervention subjects. Conclusions: Co-infection, fever duration, erythrocyte sedimentation rate, interleukin-8, and lactate dehydrogenase were identified as independent risk factors for SCPP in children. The constructed SCPP nomogram model for children based on the above risk factors has good predictive accuracy and provides support for clinicians to identify children with SCPP at an early stage.

To develop and internally validate a machine-learning-based nomogram for predicting trifecta achievement in patients undergoing robot-assisted partial nephrectomy (RAPN). This retrospective single-center study included 426 patients who underwent RAPN between 2011 and 2025 years. Trifecta was defined as negative surgical margins, warm ischemia time ≤25 minutes, and absence of perioperative complications. Variable selection was performed using the Least Absolute Shrinkage and Selection Operator (LASSO) logistic regression. Selected predictors were incorporated into a multivariate logistic regression model to construct a nomogram. Model performance was assessed using discrimination, calibration, and decision-curve analysis (DCA) with internal bootstrap validation. Trifecta was achieved in 87.7% of patients. The total operative time, estimated blood loss, tumor location, collecting system entry, and need for perioperative blood transfusion were identified as independent predictors. The model demonstrated good discrimination (area under the curve = 0.792, 95% confidence interval: 0.738-0.845). Calibration was acceptable (bootstrap-corrected slope 0.91, intercept 0.13), and the DCA showed a higher net benefit than treat-all/none for thresholds of ∼0.10-0.75. This LASSO-based nomogram offers individualized and clinically interpretable prediction of trifecta achievement in RAPN. Integrating key surgical and tumor-related parameters has the potential to support perioperative decision-making and patient counseling. External validation through prospective multicenter studies is warranted to confirm its generalizability and facilitate clinical implementation.