Category: Statistics

Esophagus. 2026 Aug 10. doi: 10.1007/s10388-026-01238-8. Online ahead of print.

ABSTRACT

BACKGROUND: Upper gastrointestinal endoscopy traverses the full upper aerodigestive tract, unlike radiography, potentially enabling incidental detection of non-gastric malignancies. However, its population-level incidental detection rate for non-gastric upper aerodigestive tract cancers has not been systematically quantified. The aim of this study was to compare endoscopic screening with radiography and quantify detection of non-gastric upper aerodigestive tract cancers in a population-based setting.

METHODS: This population-based cohort study was conducted by linking the Okayama City municipal gastric cancer screening registry with the Kokuho Database (KDB) for fiscal years 2016-2021. Among 36,326 participants contributing 64,822 screening examinations (40,832 radiography; 23,990 endoscopy), diagnoses of oral cavity, pharyngeal, laryngeal, and esophageal cancer occurring within 2 months of screening were ascertained from the KDB. Generalized estimating equations with modified Poisson regression were used to estimate adjusted risk ratios (aRRs) comparing endoscopy with radiography.

RESULTS: Endoscopic screening was significantly associated with higher composite incidental detection rates for non-gastric upper aerodigestive tract cancers (95.9 vs. 34.3 per 100,000 examinations; aRR 2.94, 95% confidence interval [CI] 1.50-5.76; p = 0.002). Specifically, esophageal cancer detection was markedly higher with endoscopy (83.4 vs. 17.1 per 100,000; aRR 5.16, 95% CI 2.16-12.32; p < 0.001). No statistically significant differences were observed for oral cavity, pharyngeal, or laryngeal cancers.

CONCLUSIONS: Endoscopic gastric cancer screening is associated with substantially higher incidental detection of non-gastric upper aerodigestive tract cancers, particularly esophageal cancer. These findings suggest an additional detection value for endoscopy that extends beyond its primary gastric cancer target in organized screening programs.

PMID:42573963 | DOI:10.1007/s10388-026-01238-8

Environ Sci Pollut Res Int. 2026 Aug 10. doi: 10.1007/s11356-026-38124-w. Online ahead of print.

ABSTRACT

The transfer of pesticides to Aiguillon Bay, a major coastal ecosystem on the Atlantic coast of France, was investigated in relation to watershed characteristics, agricultural pressure, and hydrological dynamics. The bay receives inputs from three main rivers (Sèvre Niortaise, Lay, and Curé) as well as from the Vieux channel, a downstream branch of the Lay watershed characterized by distinct land-use and drainage features. This study combined spatial land-use analysis with contamination indicators to clarify pesticide transfer pathways and associated ecological risks in intensively cultivated sub-basins. Monthly surface water samples were analyzed using LC-MS/MS and GC-MS/MS. A Proximity Indicator was developed to identify high-pressure agricultural zones adjacent to watercourses. Individual Risk Quotients (RQs) were also calculated by comparing measured environmental concentrations with Predicted No-Effect Concentrations (PNECs) to evaluate the ecological risk associated with selected pesticide compounds, and the Cumulative Toxic Pressure Index (CTPI) was applied to assess mixture toxicity. CTPI analysis revealed recurrent exceedances of the toxicity threshold (CTPI > 1) across all monitored systems, with strong seasonal variability linked to hydrological conditions. The Sèvre Niortaise and Vieux channel exhibited sustained mixture pressure, whereas the Lay showed pronounced event-driven peaks associated with rainfall episodes. Despite its smaller size, the Curé watershed displayed disproportionately high toxic pressure, reflecting strong hydrological connectivity and cereal-dominated land use. Herbicides and their metabolites were the primary contributors to mixture toxicity, including the persistent metabolite chlorothalonil R471811, frequently detected despite its regulatory ban in 2020, suggesting legacy contamination and progressive remobilization. Although individual Risk Quotients indicated negligible ecological risk for the selected compounds, CTPI revealed repeated mixture toxicity exceedances, demonstrating that cumulative effects represent the primary ecological pressure within the watershed. By integrating land-use characterization, hydrological analysis, statistical comparison, mixture toxicity assessment, and ecological risk evaluation, this study provides a comprehensive framework for understanding pesticide transfer to protected coastal ecosystems and supports the development of more effective watershed management and monitoring strategies.

PMID:42573961 | DOI:10.1007/s11356-026-38124-w

Sleep Breath. 2026 Aug 10;30(4):236. doi: 10.1007/s11325-026-03781-1.

ABSTRACT

INTRODUCTION: Obstructive sleep apnea (OSA) commonly coexists with Restless Legs Syndrome (RLS). Positive Airway Pressure (PAP) treatment for coexisting OSA may alleviate RLS symptoms. However, RLS often contributes to insomnia, leading to difficulties with sleep initiation and maintenance that may compromise PAP adherence. Objective long-term adherence data in patients with comorbid RLS and OSA remains limited. This study evaluated PAP therapy adherence and its determinants in patients with comorbid RLS and OSA.

METHODS: This retrospective cohort study included patients with RLS who reported snoring and underwent overnight polysomnography (PSG) from Jan 2022 to July 2024, at the Division of Sleep Medicine in the Peking University People’s Hospital. Clinical features and objective PAP adherence were collected. And factors influencing adherence were assessed through semi-structured interviews. Patients were followed up 1 year after PAP initiation. Good adherence was defined as device use for ≥ 4 h/night on ≥ 70% of nights.

RESULTS: Among 114 patients with RLS, 84 (73.7%) had comorbid OSA. Of these, 70 initiated PAP therapy, 37 acquired devices and only 25 provided objective adherence data. The proportion of adherent users declined progressively: 76% at 1 week, 36% at 1 month, 20% at 3 months, 16% at 6 months and 12% at 1 year. Main influencing factors included a perceived lack of need or unwillingness to continue PAP treatment, device intolerance and socioeconomic burden.

CONCLUSIONS: Long-term PAP adherence appears suboptimal in this cohort of patients with comorbid RLS and OSA. Interventions targeting sleep symptom relief, device tolerability and socioeconomic factors may improve treatment adherence.

PMID:42573935 | DOI:10.1007/s11325-026-03781-1

Drug Saf. 2026 Aug 10. doi: 10.1007/s40264-026-01696-0. Online ahead of print.

ABSTRACT

BACKGROUND AND AIM: Pharmacovigilance is essential to ensuring patient safety by enabling timely identification of adverse reactions in increasingly complex and voluminous data. Routine quantitative signal detection methods generate statistical alerts for product-event pairs based on predefined criteria; however, most alerts do not warrant further investigation, creating inefficiencies and significant time demands for pharmacovigilance teams. Manual triage of these alerts is often resource-intensive, prone to variability, and challenging to audit, highlighting the need for more reliable, transparent and efficient triage strategies. This study aimed to design, develop and prospectively evaluate an explainable Machine Learning for Intelligent Triage (MLIT) tool to assist pharmacovigilance teams in reviewing statistical alerts for vaccine and drug portfolios. The objective was to enhance signal detection performance without increasing the risk of missing signals, improving operational efficiency and maintaining decision traceability and regulatory compliance.

METHODS: Alert and individual case safety report data were retrieved from the company’s safety and signal management databases. Feature selection was guided by prior experience with a published case completeness tool, called Clinical Utility Score for Prioritisation (CUSP), and expert input. Of several ML methods explored, eXtreme Gradient Boosting (XGBoost) emerged as the optimal algorithm, with models trained and tested using a 75/25 split dataset. Iterative model refinement was conducted using Shapley Additive Explanations analyses to ensure explainability and alignment with safety reviewers’ decision-making processes. Refined models underwent prospective validation in two four-month prospective validation studies, covering over 20 products across vaccine and drug portfolios. The prospective validations assessed concordance between model predictions and reviewers’ decision under real-world conditions, as well as estimated time savings.

RESULTS: The vaccine model demonstrated robust predictive performance, achieving a weighted-average F1 score of 0.81 and an accuracy of 0.79. In the prospective validation phase, 92% of vaccine alerts were closed in alignment with the model’s top-ranked prediction, while 98% were closed within the top 3 predictions. The MLIT tool also identified inconsistencies and human errors in manual triage, highlighting its potential role as a quality-control mechanism. Safety reviewers reported a 24% reduction in time spent on triage activities, and explainability analyses confirmed that the model’s decision-making was conceptually aligned with safety reviewers’ logic. Comparable results were observed for the drug portfolio.

CONCLUSION: This study highlights the potential of ML-based tools to improve pharmacovigilance by enhancing signal detection performance, reducing the likelihood of missed signals, while increasing operational efficiency, and strengthening reproducibility and transparency. While MLIT demonstrated high concordance with expert decisions and provided meaningful time savings, human oversight remains essential, especially for low-confidence predictions. Ongoing refinement and user engagement will be critical for broader implementation and further automation, marking a significant step forward in ensuring safer and more efficient drug safety surveillance.

PMID:42573921 | DOI:10.1007/s40264-026-01696-0

Odontology. 2026 Aug 10. doi: 10.1007/s10266-026-01531-z. Online ahead of print.

ABSTRACT

This study aims to compare the diagnostic accuracy, appropriateness of treatment planning, and source citation performance of five large language models ChatGPT-4o (Free), ChatGPT-5.1 Plus, Microsoft Copilot, Google Gemini, and DeepSeek-R1 in root resorption scenarios. In December 2025, twelve clinical scenarios were created based on the classification of the European Society of Endodontology and each scenario was presented to all chatbots over four consecutive days. All responses were evaluated using a blinded assessment protocol and a binary scoring system. A total of 720 observations (12 cases × 4 repetitions × 3 criteria per model) were analyzed. The collected data were analyzed using chi-square, Fisher’s exact, and Cochran Q tests. In terms of diagnostic accuracy, Microsoft Copilot (79.2%), ChatGPT-5.1 (77.1%), and ChatGPT-4o (Free) (75%) showed the highest performance. Google Gemini (68.8%) demonstrated a moderate level of accuracy, while DeepSeek (39.6%) showed markedly low performance. All models exhibited high accuracy in treatment plan recommendations, and no statistically significant differences were detected. Regarding citation accuracy, Copilot ranked first with 100% accuracy. Although large language models present potential as supportive decision-making tools in the evaluation of root resorption, diagnostic inconsistencies, limitations in source accuracy, and variability in responses restrict their independent use in clinical applications. Therefore, the outputs generated by these models should be interpreted cautiously within clinical decision-making processes.

PMID:42573919 | DOI:10.1007/s10266-026-01531-z

Conserv Biol. 2026 Aug 10:e70367. doi: 10.1111/cobi.70367. Online ahead of print.

ABSTRACT

Identifying ecologically matching areas is both challenging and critical for understanding spatial patterns of biodiversity and advancing conservation planning. However, existing approaches often lack spatial flexibility, overlook shape and size constraints, or rely on limited environmental parameters, reducing their effectiveness in real-world applications. To address these limitations, we developed and tested a four-step framework for identifying ecologically and spatially matching areas. Our framework method involved defining the parameters for the comparative analyses; assessing similar environmental conditions between target protected areas and potential matching areas, through a modified version of the multivariate environmental similarity surface (mMESS); simulating randomly distributed polygons that mirror both the shape and surface area of the target protected area; and integrating the simulated polygons with the resulting mMESS to identify areas where environmental similarity, shape, and surface match. Finally, we applied decision criteria to identify real-world matching areas. We tested our approach in both the Northern and the Central Apennines (Italy), comparing a national park in each of these two regions to similar but unprotected areas-specifically excluding national parks, regional parks, and Natura 2000 Network sites. Our framework offers a robust tool for identifying matching and mismatching areas and enhancing our understanding of these areas’ spatial distributions and environmental conditions to support conservation planning and management. The framework may be applied to a broad range of ecological studies at various scales and has the potential to guide future conservation efforts and policies.

PMID:42572926 | DOI:10.1111/cobi.70367

Stat Med. 2026 Aug;45(18-19):e70690. doi: 10.1002/sim.70690.

ABSTRACT

Evaluating diagnostic accuracy for multi-category outcomes remains a significant challenge, primarily due to computational limitations in existing performance metrics. The Polytomous Discrimination Index (PDI) has emerged as an order-agnostic solution suitable for nominal classifications. However, its broader adoption has been constrained by the lack of efficient implementation, especially for its variance estimation, which typically would require computationally intensive bootstrapping procedures. In this work, we address this limitation by proposing a novel asymptotic variance estimator for the PDI. Our method integrates classical U -statistic theory with recent advances in combinatorics, offering a scalable and theoretically grounded alternative. To assess the performance of the proposed approach, we conduct extensive simulation studies and observe remarkable gain in computing time. We further apply our method to a real-world brain image analysis where deep neural networks are used as a diagnostic tool. We can efficiently report the accuracy of the neural networks with different depth specifications.

PMID:42572921 | DOI:10.1002/sim.70690

Laryngoscope. 2026 Aug 10. doi: 10.1002/lary.70807. Online ahead of print.

ABSTRACT

OBJECTIVES: Retrograde cricopharyngeus dysfunction (R-CPD) is a form of cricopharyngeal achalasia in which the inability to burp leads to gaseous esophageal distension, abdominal bloating, throat gurgling, and excessive flatulence. Botulinum toxin (BTX) injection of the cricopharyngeus (CP) muscle has emerged as an effective treatment, but the symptom burden of R-CPD and treatment effects on health-related quality of life (HRQoL) remain incompletely characterized.

METHODS: In this prospective cohort study, patients undergoing CP BTX treatment for R-CPD were enrolled. Participants received an initial CP injection of 75-100 units of onabotulinumtoxinA under general anesthesia; some underwent a second injection for incomplete response. HRQoL was assessed using the Short Form-36 (SF-36) survey at baseline and 6 months after treatment. Demographic and clinical data were collected, and Welch t-tests and subgroup analyses were performed.

RESULTS: Pre- and post-treatment SF-36 data were available for 25 participants. Significant improvements were observed in 6 of 8 SF-36 domains: role limitations due to physical health, bodily pain, general health, social functioning, role limitations due to emotional problems, and emotional well-being. Both Physical and Mental Component Summary Scores improved significantly after treatment. Subgroup analyses identified numerical differences by sex and psychiatric history, but none remained statistically significant after correction for multiple comparisons.

CONCLUSION: BTX treatment for R-CPD was associated with improvement in HRQoL. These findings support recognition of R-CPD and overall understanding of its burden on patients. Limitations include small sample size and selection bias, underscoring the need for larger longitudinal studies to confirm these findings and assess long-term outcomes.

PMID:42572920 | DOI:10.1002/lary.70807

Obesity (Silver Spring). 2026 Aug 10. doi: 10.1002/oby.70275. Online ahead of print.

ABSTRACT

OBJECTIVE: This study aimed to explore patients’ perspectives on GLP-1 receptor agonists (GLP-1RAs) for weight loss across the continuum of contemplating use to discontinuation, with and without achieving weight loss goals.

METHODS: From June to October 2025, participants completed a 30-item survey and ~30-question semi-structured interview regarding their perceptions of GLP-1RAs for weight loss. Interviews were tailored to six GLP-1RA groups: (1) considering use; (2) < 3 months on therapy; (3) ≥ 3 months on therapy without achieving weight loss goal; (4) ≥ 3 months on therapy with achieving weight loss goal; (5) discontinued without achieving goal; and (6) discontinued after achieving goal. Qualitative coding and thematic analysis were conducted in Dedoose (9.0.107); quantitative analyses used SAS 9.4.

RESULTS: A total of 185 participants consented, and 141 completed data collection (76%). Descriptive profiles suggested heterogeneity across psychosocial and behavioral domains. Groups 3 and 4 showed stronger profiles (higher perceived competence, intrinsic motivation, and self-monitoring). After false discovery rate adjustment across 78 comparisons, perceived competence was the only statistically significant differentiator: Group 1 was lower (z = -3.15; q = 0.0347) and Group 4 higher (z = 4.44; q < 0.001).

CONCLUSIONS: Findings underscore the importance of tailoring behavioral support to individuals’ stage of engagement in GLP-1RA-supported weight loss journeys.

PMID:42572911 | DOI:10.1002/oby.70275

Twin Res Hum Genet. 2026 Aug 10:1-3. doi: 10.1017/thg.2026.10092. Online ahead of print.

ABSTRACT

Haldane (J. B. S.) was a brilliant scientist. His ‘cost of natural selection’ is a view of the dark underbelly of Darwin’s creation – a count of the individuals who fall by the wayside when one allele displaces another. Th. Dobzhansky, perhaps not liking Haldane’s brand of mathematics, called in the eminent European William Feller to scrutinize Haldane’s cost. Feller published two articles (1966, 1967). The second sentence of the first paper shows that Feller misunderstood what Haldane measured: ‘These authors [Haldane and M. Kimura] introduce a measure for the loss in population size caused by a slow natural selection.’ Feller made intricate calculations of population size under various assumptions. These missed the point of Haldane’s clever notion. Feller’s two articles are an attempted demolition of conventional population genetics methodology.

PMID:42572908 | DOI:10.1017/thg.2026.10092