Tag: statistics

JMIR Med Educ. 2025 Nov 6;11:e77564. doi: 10.2196/77564.

ABSTRACT

BACKGROUND: Global health care education that cultivates international orientation is important for providing medical care in consideration of diverse backgrounds and collaboration with foreign medical professionals. Virtual international exchange programs could be a new type of global education in the present postpandemic era.

OBJECTIVE: This study aimed to examine the effectiveness of a virtual international exchange program in fostering quality academic and professional learning and international orientation from student perspectives across 5 countries. This research is expected to contribute to education for the development of global human resources in the health professions.

METHODS: This quasi-experimental study used a before-and-after design using a convergent parallel mixed methods approach. In this study, a 5-day interactive virtual program was offered to occupational therapy students from Bangladesh, Indonesia, Japan, the Philippines, and Thailand. The students were asked about their expectations and international orientation before the program, and about their evaluation of the program and international orientation afterward. Numerical data from a questionnaire on program expectations and evaluations were analyzed using descriptive statistics. Data on international orientation were subjected to qualitative analysis using steps for coding and theorization.

RESULTS: In total, 29 students participated in the program, out of which 12 students (response ratio 41.4%) answered the research questionnaires both before and after the program. Overall, the students’ expectations of the program were met in terms of expertise, scientific learning skills, and group interactions. Comparing before and after the program, mean scores of how the program met expectations increased, and the mean scores after the program in all 12 items asking about program evaluation were from 3.8 (SD 1.19) to 4.9 (SD 0.67; range: score 1 [lowest]-5 [highest]). Even though their motivation for participating in the program was not specific before the program, after the program, they reported having a more concrete image and specific form of what they learned from an international perspective. The participants enjoyed communication with others from diverse backgrounds while recognizing the difficulty of understanding different values. They also expressed satisfaction with their understanding of occupational therapy professionals and diverse societies, including medical systems from other countries.

CONCLUSIONS: Even though the analyzed sample data were small, these findings suggest that the program in this study may provide the participants with valuable opportunities. The virtual exchange program could foster students to cultivate qualities such as problem-finding or problem-solving and having interactions with groups from diverse backgrounds.

PMID:41197114 | DOI:10.2196/77564

JMIR Med Inform. 2025 Nov 6;13:e73605. doi: 10.2196/73605.

ABSTRACT

BACKGROUND: Manual review of electronic health records for clinical research is labor-intensive and prone to reviewer-dependent variations. Large language models (LLMs) offer potential for automated clinical data extraction; however, their feasibility in surgical oncology remains underexplored.

OBJECTIVE: This study aimed to evaluate the feasibility and accuracy of LLM-based processing compared with manual physician review for extracting clinical data from breast cancer records.

METHODS: We conducted a retrospective comparative study analyzing breast cancer records from 5 academic hospitals (January 2019-December 2019). Two data extraction pathways were compared: (1) manual physician review with direct electronic health record access (group 1: 1366/3100, 44.06%) and (2) LLM-based processing using Claude 3.5 Sonnet (Anthropic) on deidentified data automatically extracted through a clinical data warehouse platform (group 2: 1734/3100, 55.94%). The automated extraction system provided prestructured, deidentified data sheets organized by clinical domains, which were then processed by the LLM. The LLM prompt was developed through a 3-phase iterative process over 2 days. Primary outcomes included missing value rates, extraction accuracy, and concordance between groups. Secondary outcomes included comparison with the Korean Breast Cancer Society national registry data, processing time, and resource use. Validation involved 50 stratified random samples per group (900 data points each), assessed by 4 breast surgical oncologists. Statistical analysis included chi-square tests, 2-tailed t tests, Cohen κ, and intraclass correlation coefficients. The accuracy threshold was set at 90%.

RESULTS: The LLM achieved 90.8% (817) accuracy in validation analysis. Missing data patterns differed between groups: group 2 showed better lymph node documentation (missing: 152/1734, 8.76% vs 294/1366, 21.52%) but higher missing rates for cancer staging (211/1734, 12.17% vs 43/1366, 3.15%). Both groups demonstrated similar breast-conserving surgery rates (1107/1734, 63.84% vs 868/1366, 63.54%). Processing efficiency differed substantially: LLM processing required 12 days with 2 physicians versus 7 months with 5 physicians for manual review, representing a 91% reduction in physician hours (96 h vs 1025 h). The LLM group captured significantly more survival events (41 vs 11; P=.002). Stage distribution in the LLM group aligned better with national registry data (Cramér V=0.03 vs 0.07). Application programming interface costs totaled US $260 for 1734 cases (US $0.15 per case).

CONCLUSIONS: LLM-based curation of automatically extracted, deidentified clinical data demonstrated comparable effectiveness to manual physician review while reducing processing time by 95% and physician hours by 91%. This 2-step approach-automated data extraction followed by LLM curation-addresses both privacy concerns and efficiency needs. Despite limitations in integrating multiple clinical events, this methodology offers a scalable solution for clinical data extraction in oncology research. The 90.8% accuracy rate and superior capture of survival events suggest that combining automated data extraction systems with LLM processing can accelerate retrospective clinical research while maintaining data quality and patient privacy.

PMID:41197113 | DOI:10.2196/73605

JCO Clin Cancer Inform. 2025 Nov;9:e2500033. doi: 10.1200/CCI-25-00033. Epub 2025 Nov 6.

ABSTRACT

PURPOSE: Real-world data (RWD) are critical for breast cancer (BC) research but are limited by privacy concerns, missing information, and data fragmentation. This study explores synthetic data (SD) generated through advanced generative models to address these challenges and create harmonized longitudinal data sets.

METHODS: A data set of 1052 patients with human epidermal growth factor receptor 2-positive and triple-negative BC from the Informatics for Integrating Biology and the Bedside (i2b2) platform was used. Advanced generative models, including generative adversarial networks (GANs), variational autoencoders (VAEs), and language models (LMs), were applied to generate synthetic longitudinal data sets replicating disease progression, treatment patterns, and clinical outcomes. The Synthethic Validation Framework (SAFE) powered by Train was used to evaluate the fidelity, utility, and privacy. SD were tested across three settings: (1) integration with i2b2 for privacy-preserving data sets; (2) multistate disease modeling to predict clinical outcomes; and (3) generation of synthetic control groups for clinical trials.

RESULTS: The synthetic data sets exhibited high fidelity (score 0.94) and ensured privacy, with temporal patterns validated through time-series analyses and Uniform Manifold Approximation and Projection embeddings. In setting A, SD accurately mirrored RWD on the i2b2 platform while maintaining privacy. In setting B, incorporating SD improved the predictive performance of a multistate disease progression model, increasing the C-index by up to 10%. In setting C, SD replicated the end points of the APT trial, demonstrating its feasibility for generating synthetic control arms with preserved statistical properties of the real data set.

CONCLUSION: AI-generated longitudinal SD effectively address key challenges in RWD use in BC. This approach can improve translational research and clinical trial design while ensuring robust privacy protection. Integration with platforms such as i2b2 highlights their scalability and potential for broader applications in oncology.

PMID:41197110 | DOI:10.1200/CCI-25-00033

N Z Med J. 2025 Nov 7;138(1625):13-19. doi: 10.26635/6965.7129.

ABSTRACT

AIM: Endoscopic submucosal dissection (ESD) has become a well-established treatment option for premalignant and early malignant lesions of the gastrointestinal tract. This study aimed to evaluate the recurrence rate following ESD in a single tertiary centre cohort of patients.

METHODS: All consecutive patients who received ESD treatment for premalignant or early malignant lesions by a single endoscopist (AS) at Middlemore Hospital from 11 February 2019 to 6 October 2023 were included in this retrospective cohort study. The primary outcome was recurrence rate of premalignant and early malignant lesions of the gastrointestinal tract following ESD. Recurrence was defined as confirmed neoplasm on histopathology on first follow-up surveillance endoscopy. The target recurrence rate was less than or equal to 5%. Secondary outcome was recurrence stratified by location of the lesion, lesion size, en bloc resection status, R0 resection status and histopathological type of lesion.

RESULTS: A total of 119 ESD procedures were completed during the study time frame, with 91 having a surveillance endoscopy with a median time of 231 days. Twenty-eight cases did not have surveillance endoscopy completed. Three (3.3%) had recurrence of disease, of which two were oesophageal squamous cell carcinoma and one was rectal sessile serrated adenoma. We were unable to ascertain any statistically significant associations with regard to our secondary outcome variables.

CONCLUSION: This study supports the efficacy of ESD in our centre as a curative treatment modality for premalignant and early malignant gastrointestinal lesions, demonstrating a recurrence rate within the acceptable international benchmark.

PMID:41197092 | DOI:10.26635/6965.7129

Top Stroke Rehabil. 2025 Nov 6:1-16. doi: 10.1080/10749357.2025.2585118. Online ahead of print.

ABSTRACT

BACKGROUND: Neurological damage resulting from stroke can impair the respiratory system, leaving stroke patients susceptible to pulmonary complications, such as stroke-associated pneumonia (SAP). Due to the substantial mortality risk associated with SAP and its complications, we conducted a systematic review and meta-analysis to estimate the global prevalence of SAP.

METHOD: A comprehensive search of MEDLINE via PubMed, EMBASE, Scopus, and Web of Science was conducted from 1 January 2014 to 7 August 2024. The cross-sectional studies were selected. The primary study outcome was the prevalence of SAP. Subgroup analysis was implemented. The random-effects method was used to implement meta-analysis. The Joanna Briggs Institute tool for prevalence was used to assess risk of bias in the included studies.

RESULT: A comprehensive search of MEDLINE via PubMed, EMBASE, Scopus, and Web of Science was conducted from 1 January 2014 to 7 August 2024. The cross-sectional studies were selected. The primary study outcome was the prevalence of SAP. Subgroup analysis was implemented. The random-effects method was used to implement meta-analysis. The Joanna Briggs Institute tool for prevalence was used to assess risk of bias in the included studies. Result The present systematic review and meta-analysis integrated data from 24 studies, encompassing 4,272,805 participants. The prevalence of SAP was 18.3% (95% CI: 13.7-23.0). Results showed significant heterogeneity among studies regarding prevalence estimates (I2= 99.74%, p<0.001). Subgroup analysis by country revealed a substantial reduction in heterogeneity within one specific subgroup (I2=0.00, P=0.67). Utilizing Begg’s and Egger’s tests showed no statistically significant evidence (P = 0.9013 and P = 0.8398, respectively) of publication bias. The trim-and-fill analysis did not impute additional studies, implying that publication bias is unlikely. A leave-one-out sensitivity analysis revealed that excluding the study by Asgedom et al. resulted in the most significant change in the overall prevalence of SAP.

CONCLUSION: Our results emphasized the need to effectively identify and manage risk factors to reduce the likelihood of SAP.

PMID:41197074 | DOI:10.1080/10749357.2025.2585118

WMJ. 2025;124(4):371-374.

ABSTRACT

INTRODUCTION: HIV remains a significant public health concern. In Wisconsin, new cases increased by 36% during 2020 through 2022, and 22% were 13 to 24 years old. Despite recommendations for routine HIV screening, youth testing remains inadequate. This study aimed to understand HIV screening practices among youth receiving care in pediatric primary care clinics in southeastern Wisconsin.

METHODS: Clinic HIV testing rates were measured in patients aged 12 to 26 undergoing gonorrhea and/or chlamydia testing at pediatric primary care clinics affiliated with a not-for-profit children’s hospital.

RESULTS: Youth HIV testing rates at all clinic sites were low (median 19.7%) ranging from 13.2% to 36.1%. Higher rates were seen in clinics with higher rates of sexually transmitted infections.

CONCLUSIONS: Interventions are needed to enhance HIV testing rates in pediatric primary care clinics.

PMID:41197057

WMJ. 2025;124(4):357-363.

ABSTRACT

BACKGROUND: Housing and health care both play crucial roles in overall health. Though housing and health care barriers negatively impact affect health, little is known about the relative influence of each. This study sought to understand the relationship between housing circumstance, barriers to care, and mental health outcomes among low-income, uninsured patients seen at a free clinic in Milwaukee, Wisconsin. This includes investigating the relative impact of risk factors for homelessness, housing barriers, and health care barriers on mental health.

METHODS: Surveys were administered to clinic patients (n = 94) from June to December 2023. Surveys assessed patient demographics, housing and health care barriers, and mental health outcomes, primarily measured by the Patient Health Questionnaire-2 (PHQ-2), General Anxiety Disorder-2 (GAD-2) questionnaire, modified loneliness scale, and individuals’ subjective mental health rating.

RESULTS: Increased health care barriers and socioenvironmental risk factors for homelessness significantly predicted worse PHQ-2 score, GAD-2 score, loneliness, and mental health rating. Despite significant associations, increased housing barriers did not significantly predict any of the 4 mental health metrics. Furthermore, neither housing barriers nor health care barriers significantly predicted recreational drug use, whereas socioenvironmental risk factors for homelessness were both a significant predictor and response of increased recreational drug use. The most frequently reported mental health care barriers were insurance coverage, financial barriers, and transportation issues. In addition, there was significantly lower patient trust in mental health care providers than in general medical providers, which may reflect increased stigma.

CONCLUSIONS: Compared to housing barriers, increased health care barriers significantly predicted worse mental health outcomes. This study emphasizes the importance of addressing health care barriers to improve mental health.

PMID:41197054

Aging Clin Exp Res. 2025 Nov 6;37(1):314. doi: 10.1007/s40520-025-03215-0.

ABSTRACT

BACKGROUND: Sarcopenic obesity (SO) is increasingly recognized as a significant health concern, particularly among older populations. Existing literature indicates that SO elevates the risk for various adverse health outcomes such as cardiovascular diseases, fractures, higher all-cause mortality. However, evidence regarding its impact on the risk of falls remains limited and inconclusive. Our study aimed to investigate the association between SO and fall incidents.

METHODS: A total of 10,905 participants were enrolled from the baseline survey of the China Health and Retirement Longitudinal Study (CHARLS) 2015 wave. Participants were categorized into four groups according to sarcopenia and obesity status, with the neither sarcopenia nor obesity group serving as the reference. Logistic regression was utilized to evaluate the cross-sectional association between SO and falls. Furthermore, we tracked fall incidents reported in follow-up surveys conducted in CHARLS 2018 and 2020 wave. Cox regression analysis was performed to explore how SO affected the risk of falls. Stratified Cox analyses by age (< 60 vs. ≥60 years) were also performed.

RESULTS: In the cross-sectional analysis (2015), the SO group [OR (95% CI): 1.84 (1.42 ~ 2.37), P < 0.01] showed a higher risk of falls compared to the reference group; however, this association was not statistically significant after adjusting for potential confounding factors. In the longitudinal analysis (2015-2020), the SO group [HR (95%CI): 2.78 (2.03 ~ 3.80), P < 0.01] had a significantly increased risk of falls. The results remained similar after adjusting for age, sex [HR (95%CI): 1.44 (1.02 ~ 2.04), P < 0.05], and additional covariates [HR (95%CI): 1.43 (1.00 ~ 2.03), P < 0.05]. Notably, stratified Cox models showed that SO was significantly associated with fall risk in both age groups, with a stronger effect observed in participants under 60 years [HR (95%CI): 2.85 (1.13 ~ 7.17), P < 0.05] than in those aged 60 and above [HR (95%CI): 1.64 (1.08 ~ 2.50), P < 0.05].

CONCLUSION: Sarcopenic obesity is associated with an increased risk of falls among middle-aged and older adults, especially in longitudinal analyses. Age-stratified results suggest that the impact of SO on falls may be more pronounced in the middle-aged group. Our findings support the need for early identification and targeted interventions for individuals with SO to mitigate fall-related risks in aging populations.

PMID:41196484 | DOI:10.1007/s40520-025-03215-0

Genes Genomics. 2025 Nov 6. doi: 10.1007/s13258-025-01693-0. Online ahead of print.

ABSTRACT

BACKGROUND: The high prevalence of non-biological zero counts, arising from low sequencing depth and sampling variation, presents a significant challenge in microbiome data analysis. These zeros can distort taxon abundance distributions and hinder the identification of true biological signals, complicating downstream analyses.

OBJECTIVE: To address the challenges of non-biological zeros in microbiome datasets, we propose DeepIMB, a deep learning-based imputation method for microbiome data, specifically designed to accurately identify and impute non-biological zero counts while preserving biological integrity.

METHODS: DeepIMB operates in two main phases. First, it identifies non-biological zeros using a gamma-normal mixture model applied to the normalized, log-transformed taxon count matrix. Second, it imputes these zeros with a deep neural network model that integrates diverse sources of information, including taxon abundances, sample covariates, and phylogenetic distances, thereby learning complex, nonlinear relationships within microbiome data.

RESULTS: By leveraging integrated information from multiple data types, DeepIMB accurately imputes non-biological zeros while preserving true biological signals. In our two simulation studies, DeepIMB outperformed existing imputation methods in terms of mean squared error, Pearson correlation coefficient, and Wasserstein distance.

CONCLUSION: DeepIMB effectively addresses the challenges posed by non-biological zeros in microbiome data. By improving the quality of the data and the reliability of downstream analyses, DeepIMB represents a significant advancement in microbiome research methodologies.

PMID:41196474 | DOI:10.1007/s13258-025-01693-0

Clin Transl Oncol. 2025 Nov 6. doi: 10.1007/s12094-025-04093-8. Online ahead of print.

ABSTRACT

BACKGROUND: Early detection of metastasis in cancer patients plays a pivotal role in improving treatment outcomes and increasing patient survival. This study aimed to evaluate the predictive role of inflammatory indices, including neutrophil-to-lymphocyte ratio (NLR), platelet-to-lymphocyte ratio (PLR), monocyte-to-lymphocyte ratio (MLR), systemic immune inflammation index (SII), and systemic inflammation response index (SIRI), in identifying metastatic status.

METHODS: In this study, 60 cancer patients were enrolled between December 2023 and June 2024. Clinicopathological data and complete blood counts (CBCs) were collected prior to treatment initiation. The Receiver Operating Characteristic (ROC) curve was used to determine the optimal cutoff values of different baseline inflammatory indices for the metastatic status analysis.

RESULTS: The levels of inflammatory indices were greater in metastatic patients than in nonmetastatic patients; however, only the SIRI was significantly different (1.04 [0.76-1.69] vs. 0.71 [0.45-1.07]; P = 0.044). ROC curve analysis revealed that the area under the curve (AUC) for the SIRI was 0.652 (95% CI 0.507-0.797). Furthermore, broader combinations of the SIRI and MLR, either individually or in conjunction with the NLR, PLR, and/or SII, yielded multi-index models with greater discriminatory power and maintained statistical significance (p < 0.05).

CONCLUSION: The findings indicate that the SIRI, in combination with the MLR, plays a significant role in predicting the metastatic status of cancer patients.

PMID:41196459 | DOI:10.1007/s12094-025-04093-8