Category: Statistics

JMIR Form Res. 2026 Apr 28;10:e86203. doi: 10.2196/86203.

ABSTRACT

BACKGROUND: Personal digital health technologies (DHTs) enable real-time monitoring of physiological metrics and behavioral data, including heart rate variability (HRV), supporting analysis of pregnancy-related conditions and personalized care throughout the perinatal period. While recent studies demonstrate the utility of personal DHTs in tracking pregnancy-related symptoms, they often rely on aggregate statistical methods that overlook individual variability.

OBJECTIVE: This study aims to compare aggregate and individual-level analyses of DHT data for pregnancy-related conditions, using the comprehensive BUMP (Better Understanding the Metamorphosis of Pregnancy) dataset to highlight the importance of individual variability and data heterogeneity.

METHODS: We analyzed wearable and self-reported data from 256 participants enrolled in the BUMP study (January 2021 to May 2022), including HRV, sleep, and fatigue measured via Oura Rings and smartphone surveys. Individual-level (N-of-1) trajectories were evaluated and compared with aggregate results to uncover personal and collective trends. A statistical method was developed to assess the influence of adverse events and severe symptoms, while case studies explored confounding and modifying factors underlying heterogeneity. Comprehensive statistical analysis included the coefficient of determination, Kolmogorov-Smirnov tests, likelihood ratio tests, and Welch t tests, with interindividual variability flagged based on high-variability thresholds.

RESULTS: Substantial interindividual variability was observed across all features. Only 4.76% (12/256) of participants exhibited an HRV inflection at the aggregate week-33 inflection point, with a coefficient of variation of 14.24%. The median value of the gestational week in individual fatigue troughs was 23 (IQR 8; range 8-38) weeks, differing from aggregate estimates. Distributional comparisons showed no statistically significant differences in individual-level model fit (R²) by pregnancy complications or age (P values ranging from .06 to .99 across all model fit comparisons). Case studies further highlighted both intraindividual and interindividual differences, emphasizing the importance of considering external factors, such as adverse events and severe symptoms.

CONCLUSIONS: Our findings show that aggregate wearable data often fail to generalize across populations, oversimplifying pregnancy-related physiological and subjective changes. This simplification can obscure individual trajectories, leading to generalized insights that may not reflect many pregnant women’s experiences. Our results highlight the impact of heterogeneity on pregnancy outcomes, emphasizing the need to move beyond one-size-fits-all models and leverage DHT for personalized care.

PMID:42048637 | DOI:10.2196/86203

J Phys Chem B. 2026 Apr 28. doi: 10.1021/acs.jpcb.6c01076. Online ahead of print.

ABSTRACT

Elucidating the microscopic clustering of molecules is pivotal to understanding the macroscopic behavior of complex liquids. However, resolving specific cluster contributions from congested vibrational signatures remains a formidable challenge due to severe spectral overlap. Herein, we present a high-throughput computational framework that integrates molecular dynamics sampling and density functional theory calculations with constrained geometry optimization to reconstruct infrared (IR) and excess IR spectra. By statistically weighting the spectra of thousands of cluster isomers, this method quantitatively reproduces experimental measurements without relying on a priori structural assumptions. Applied to TBA-CCl₄ mixtures, it shows that the concentration-dependent IR changes are governed by the redistribution of cluster populations rather than simple hydrogen-bond weakening. Experimentally, this evolution is reflected mainly in band-shape changes and the gradual emergence of a high-frequency shoulder. Furthermore, we demonstrate that the complex features in the excess IR spectra arise not from a single dominant species but from the cooperative contributions of multiple coexisting hydrogen-bonded networks, specifically the competitive balance between the formation of small chain oligomers and the dissociation of large size clusters. This spectroscopically driven strategy provides a robust tool for deconvoluting molecular-level heterogeneity in complex condensed phases, effectively bridging the gap between atomistic simulations and spectroscopic observables.

PMID:42048632 | DOI:10.1021/acs.jpcb.6c01076

Nurs Open. 2026 May;13(5):e70535. doi: 10.1002/nop2.70535.

ABSTRACT

AIM: This study aimed to examine the supportive care needs of family caregivers of elderly patients with dementia and diabetes mellitus, and identify the associated factors to provide a scientific basis for the development of effective supportive care interventions.

DESIGN: Cross-sectional study.

METHODS: Using convenience sampling, we recruited 108 family caregivers of elderly patients with dementia and diabetes mellitus from five neighbourhood committees and 10 natural villages in a community in Xiamen, China. Data on caregivers’ demographics and supportive care needs were collected via questionnaires.

RESULTS: Caregivers reported high levels of need, with physiological, emotional and safety needs rated most highly. The Caregiver Burden Inventory score was a significant positive predictor of physiological, informational, safety, emotional and spiritual needs, but not of social needs. Physiological needs were associated with the caregiver’s occupation and economic status; informational needs with sex and education level; safety needs with sex and occupation; spiritual needs with age and economy; and emotional and social needs with cohabitation status, marital status and relationship to the patient.

CONCLUSIONS: Caregiver burden is a key factor associated with the supportive care needs of family caregivers of older adults with dementia and diabetes mellitus. Future interventions should consider both caregiver burden and individual characteristics to provide targeted, multi-level support.

PATIENT OR PUBLIC CONTRIBUTION: No patient or public contribution.

IMPLICATIONS: Assessment of caregiver burden should be integrated into community health practice to guide support strategies for caregivers of patients with chronic conditions.

IMPACT: The findings will guide community health professionals and policymakers in designing support programmes for caregivers managing complex chronic conditions.

PMID:42048613 | DOI:10.1002/nop2.70535

J Org Chem. 2026 Apr 28. doi: 10.1021/acs.joc.5c03133. Online ahead of print.

ABSTRACT

A DFT mechanistic study of C-H borylation of 3-fluorobenzotrifluoride catalyzed by Ir complexes ligated by 4,4′-bis(trifluoromethyl)-2,2′-bipyridine (btfbpy), 4,4′-ditert-butyl-2,2′-bipyridine (dtbpy), and 3,4,7,8-tetramethyl-1,10-phenanthroline (tmphen), respectively, demonstrates that the C-H bonds distal to the steric -CF₃ group exhibit comparable, albeit slightly lower, rate and regioselectivity-determining barriers compared to those proximal to -CF₃, which corroborates with the experimental observations of statistical regioselectivity among aryl C-H bonds in the absence of significant steric hindrance. Conversely, utilizing a bulky spirobipyridine ligand that imposes substantial steric hindrance at the activation sphere is predicted to effectively promote meta-to-fluorine regioselectivity, which was found to arise from the stronger catalyst-substrate interactions due to the steric-screening effect, leading to the lower C-H activation barrier.

PMID:42048608 | DOI:10.1021/acs.joc.5c03133

Lab Med. 2026 Apr 3;57(3):lmag021. doi: 10.1093/labmed/lmag021.

ABSTRACT

INTRODUCTION: A collective elevation in serum uric acid levels was detected among 98 military conscripts during routine physical examinations. This study aimed to determine the underlying causes and establish a traceable laboratory quality control framework for abnormal biochemical results.

METHODS: A systematic investigation encompassing analytical performance, preanalytical variables, and possible exogenous interferences was conducted. Population characteristics, including dietary habits and physical activity, were examined and experimentally validated. Ten participants were retested after controlling high-purine intake and exercise intensity.

RESULTS: No deviations were identified in analytical systems or preanalytical procedures. No interference from exogenous substances was observed. Controlled validation experiments demonstrated that consumption of chicken liver and high-intensity exercise increased serum uric acid levels by 21.22% and 20.85%, respectively (P < .001). After intervention, serum uric acid levels decreased by 32.40% on average, with levels in 70% of participants returning to the reference range.

DISCUSSION: The generalized serum uric acid elevation in conscripts was primarily attributed to combined effects of a high-purine diet and strenuous exercise. Establishing a traceable, standardized quality management model enables laboratories to accurately identify, verify, and resolve abnormal test results, enhancing analytical reliability and clinical data integrity.

PMID:42048558 | DOI:10.1093/labmed/lmag021

JMIR Med Inform. 2026 Apr 28;14:e79972. doi: 10.2196/79972.

ABSTRACT

BACKGROUND: Computed tomography pulmonary angiography (CTPA) is the standard imaging modality for diagnosing pulmonary embolism (PE), but diagnostic uncertainty is common due to technical limitations and vague language, leading to inconsistent interpretation and clinician frustration.

OBJECTIVE: This study develops a prompt-free, data-efficient method for assessing diagnostic certainty of PE in CTPA reports using small pretrained language models.

METHODS: This study examined 173 consecutive CTPA reports from UMass Memorial Health, each annotated by 3 radiologists for PE diagnostic certainty. We developed PECertainty, a lightweight, prompt-free model, and compared it with advanced large language model (LLM)-based methods under limited supervision settings. Baselines included prompt-free methods (support vector machine, random forest, and RoBERTa [Robustly Optimized Bidirectional Encoder Representations From Transformers Pretraining Approach]) and prompt-dependent methods (LLM fine-tuning, in-context learning, and ADAPET [A Densely-Supervised Approach to Pattern Exploiting Training]; UNC Chapel Hill) with open-source Gemma3-4B (Google DeepMind) and Llama3.2-3B (Meta), and the proprietary GPT-3.5 (OpenAI). Sensitivity analyses evaluated performance with 1 to 10 training examples per category for the top performer. Model performance was evaluated against radiologist annotations. External validation on 420 CTPA reports from the Baystate Medical Center, with validation limited to distinguishing certain from uncertain reports. Interpretability of the top-performing models (PECertainty and GPT-3.5) was evaluated using integrated gradients and prompt-based explanations reviewed by radiologists.

RESULTS: Among prompt-dependent methods, GPT-3.5 fine-tuning (F1-score 0.86; 95% CI 0.71-1.0) and in-context learning (F1-score 0.87; 95% CI 0.71-1.0) performed best, and the performance of in-context learning consistently outperformed 0-shot learning for Gemma3-4B (F1-score 0.63, 95% CI 0.56-0.79 vs F1-score 0.45; 95% CI 0.29-0.56) and Llama3.2-3B (F1-score 0.54; 95% CI 0.41-0.71 vs F1-score 0.43, 95% CI 0.28-0.62). PECertainty demonstrated numerically better or equivalent performance compared with both the top-performing prompt-dependent methods and all prompt-free baselines. Compared with fine-tuned ClinicalBERT (Bidirectional Encoder Representations From Transformers Pretrained on Clinical Text), PECertainty achieved statistically significant improvements across all metrics (paired bootstrap significance test, P<.05). RoBERTa (Robustly Optimized Bidirectional Encoder Representations From Transformers Pretraining Approach) fine-tuning lagged (F1-score 0.52; 95% CI 0.35-0.71), and simple models such as support vector machine underperformed. In few-shot settings (10 examples/category), PECertainty (F1-score 0.80; 95% CI 0.59-0.94) outperformed both GPT-3.5 fine-tuning (F1-score 0.74; 95% CI 0.58-0.88) and in-context learning (F1-score 0.65; 95% CI 0.47-0.83). External validation on the Baystate dataset showed good generalization for distinguishing certain from uncertain cases (F1-score 0.77; 95% CI 0.70-0.83). Despite its strong performance, PECertainty was rated as less interpretable than fine-tuned GPT-3.5 by radiologists (t test, P<.05).

CONCLUSIONS: PECertainty enables accurate and data-efficient assessment of diagnostic certainty from free-text CTPA reports in low-resource settings. As an open-source, lightweight alternative to proprietary LLMs, it may support more precise communication between radiologists and referring physicians, with interpretability identified as a key direction for improvement.

PMID:42048554 | DOI:10.2196/79972

G3 (Bethesda). 2026 Apr 28:jkag106. doi: 10.1093/g3journal/jkag106. Online ahead of print.

ABSTRACT

Mungbean (Vigna radiata (L.) R. Wilczek) is a vital source of digestible proteins and is well-suited for the plant-based protein industry. In this study, we analyzed pod morphological traits in the Iowa Mungbean Diversity (IMD) panel of 372 genotypes (2022-23) using image-analysis-based phenotyping on 2,418 pod images. Pod morphological traits were extracted using deep learning image analysis, achieving excellent agreement with manual measurements (r>0.96 for pod length and seed per pod). Four complementary GWAS models identified 65 significant SNPs (-log10(P) ≥ 5.56) associated with pod curvature, length, width, and seed per pod traits. A significant SNP (5_35265704) on chromosome 4 was linked to pod dimensional traits, length, width, and curvature. A candidate gene, Virad04G0076900, located 15.6 kb from this SNP, is part of the GH3 gene family and has an Arabidopsis ortholog (AT4G27260) known for influencing organ elongation, pod, and seed development. Another SNP, 5_210437 on chromosome 6, has been found to be significantly associated with both pod length and seed per pod. A candidate gene, Virad06G0002400 (36.5 kb from this SNP), encodes a potassium transporter and shares homology with the Arabidopsis gene HAK5 (AT4G13420), known to influence pod growth. Image-based measurements achieved genomic prediction accuracies ranging from 0.61 to 0.85 across various traits, demonstrating comparable accuracy to manual methods for linear traits and up to 22% improvement for complex shape traits. These results highlight the potential of deep learning-assisted phenomics integrated with genomic tools to accelerate selection for improved pod architecture in mungbean breeding programs across the Midwestern United States and globally.

PMID:42048549 | DOI:10.1093/g3journal/jkag106

J Pharm Pharmacol. 2026 Apr 3;78(4):rgag037. doi: 10.1093/jpp/rgag037.

ABSTRACT

The application of Quality-by-Design approaches in the development of analytical methods has changed the way drugs are manufactured, switching from trial-and-error and one-variable-at-a-time methods to a structured, risk-based scientific framework. The Analytical Target Profile is at the heart of Quality-by-Design. It clearly lays out the criteria for how well the method will perform. The identification of Critical Quality Attributes and Critical Method Parameters through systematic risk assessment tools like the Ishikawa diagram and Failure Mode and Effect Analysis supports this. Statistical methods, particularly Design of Experiments, have been crucial for identifying and optimizing key variables. This leads to the development of a Method Operable Design Region (MODR). The MODR sets the limits for the analytical method’s reliable results, which means that changes can be made after approval without having to go through the whole process again. Box-Behnken and Central Composite are two common designs that are used to determine how various factors interact in order to ensure that methods perform effectively. Quality-by-Design-based control strategies combine lifecycle management and real-time monitoring to make sure that quality continues to improve more effectively. Literature screening and data organization were performed using Microsoft Excel (Microsoft Corporation, Redmond, WA, USA). Reference management and duplicate removal were carried out using EndNote (Clarivate Analytics). Database searches were conducted across PubMed, Web of Science, Elsevier, and Google Scholar using predefined keywords.

PMID:42048548 | DOI:10.1093/jpp/rgag037

Int Rev Psychiatry. 2026 Feb-May;38(1-3):27-36. doi: 10.1080/09540261.2025.2523455. Epub 2025 Jun 30.

ABSTRACT

Psychiatric classification systems have evolved significantly over the past three decades. This article critically examines the transition from DSM-IV and ICD-10 to the more recent DSM-5-TR and ICD-11, analysing key conceptual shifts, including the redefinition of diagnostic criteria, the inclusion of emerging disorders, and the removal of outdated or stigmatizing categories. Particular attention is paid to the growing limitations of categorical models in capturing clinical heterogeneity and comorbidity. In response, dimensional approaches and precision psychiatry have gained prominence, aiming to integrate symptom severity, biomarkers, and neurobiological correlates into diagnostic decision-making. The manuscript explores future trajectories for psychiatric nosology, including the potential role of artificial intelligence, the recognition of novel syndromes such as eco-anxiety and digital addictions, and the need for culturally sensitive frameworks. It also provides stakeholder-specific recommendations-addressed to clinicians, researchers, policymakers, and educators-summarized in a dedicated table. These forward-looking strategies emphasize the importance of ethical, interdisciplinary, and inclusive practices in updating diagnostic systems. Ultimately, the future of psychiatric classification lies in striking a balance between scientific rigor, sociocultural relevance, and individual variability, ensuring that future manuals serve both clinical utility and human dignity.

PMID:42048530 | DOI:10.1080/09540261.2025.2523455

JMIR Med Inform. 2026 Apr 28;14:e82329. doi: 10.2196/82329.

ABSTRACT

BACKGROUND: Accurate prediction of surgical duration is critical for optimizing operating room scheduling and resource allocation. Existing models, however, exhibit limited applicability in pediatric urology due to the unique anatomical and developmental characteristics of children.

OBJECTIVE: This study aimed to develop and validate a specialty-tailored prediction framework for estimating the duration of pediatric urological surgeries.

METHODS: We integrated multisource heterogeneous data, encompassing patient demographics, surgical details, surgeon-specific features, and electronic medical record narratives, to develop a customized prediction system. Large language model techniques were used to extract semantic representations from unstructured clinical text, while a multihead perceptron architecture enabled the efficient fusion of structured and unstructured features. Pediatric-specific clinical variables, such as developmental stage and the severity of urinary tract malformations, were explicitly modeled to capture their impact on surgical duration.

RESULTS: The proposed approach achieved a mean absolute error of 11.39 minutes and a root mean square error of 15.58 minutes, markedly outperforming existing methods. Comparative analyses demonstrated that the Qwen-based structured preprocessing with text embeddings provided superior feature representation, surpassing both traditional long short-term memory and direct Embedding-3 approaches. Feature importance analysis identified the primary surgical procedure, surgical plan, and preoperative diagnosis as dominant predictive factors.

CONCLUSIONS: By combining innovative feature engineering with a tailored model architecture, the proposed framework substantially improves the accuracy of surgical duration prediction in pediatric urology. These findings offer robust technical support for precision operating room scheduling and hold significant clinical value in enhancing the efficiency of surgical resource utilization.

PMID:42048521 | DOI:10.2196/82329