Category: Statistics

JMIR Hum Factors. 2026 Mar 20;13:e79866. doi: 10.2196/79866.

ABSTRACT

BACKGROUND: Living with asthma-especially in its severe forms-can significantly impact daily life, including social activities, work, travel, and household responsibilities. Collaboration between patients and health care professionals (HCPs) is frequently lacking, particularly regarding treatment goals. Self-management has been shown to mitigate the negative effects of asthma. Technical solutions might support self-management for patients with chronic diseases and their collaboration with HCPs.

OBJECTIVE: This explorative study aims to understand how patients and HCPs experience the use of a digital self-management system for asthma monitoring.

METHODS: This qualitative study was conducted at 5 primary care centers in Sweden and involved 20 participants: 14 patients who had utilized the digital self-management system Asthmatuner for at least 6 months and 6 specialist asthma nurses. Individual semistructured interviews were analyzed using qualitative content analysis to explore patterns and relationships within the data.

RESULTS: We identified 1 main theme, that is, “data-supported empowerment,” and 3 subthemes, that is, (1) empowerment by awareness, knowledge, and learning; (2) contact health care-patient; and (3) managing the monitoring. The theme of data-supported empowerment emerged as a synthesis of these findings, reflecting how the self-management system enabled patients to take a more active role in managing their medications and health. While most patients did not monitor their data continuously, they engaged with it when they felt it was necessary. Some patients expressed expectations of personalized follow-up from HCPs based on their monitoring data; however, these expectations were not always fulfilled. We also revealed a need to adapt and clarify the overlapping responsibilities of patients and HCPs.

CONCLUSIONS: The digital self-management system for asthma was well received by both patients and HCPs, as it promoted empowerment. Clear communication about changes in workflow and responsibilities is essential to ensure the successful implementation of digital systems and improved health care delivery.

PMID:41861373 | DOI:10.2196/79866

JMIR Form Res. 2026 Mar 20;10:e82685. doi: 10.2196/82685.

ABSTRACT

BACKGROUND: Extracting accurate medication information from Thai hospital records presents challenges due to the narrative style of medical notes, which often combine Thai and English terminology. Named entity recognition (NER) serves as the foundational step for advanced clinical information extraction (IE) tasks, including medical concept normalization and relation extraction. This study aimed to establish a robust NER framework to address these difficulties by leveraging ontology-based annotation and pretrained transformer models.

OBJECTIVE: The primary objective of this study was to evaluate the performance of 5 fine-tuned pretrained transformer models-BioClinicalBERT, ClinicalBERT, PubMedBERT, MultilingualBERT, and ThaiBERT-based on Bidirectional Encoder Representations from Transformers (BERT) in extracting structured medication information from unstructured Thai hospital discharge summaries.

METHODS: Ninety discharge summaries were collected from Maharaj Nakhon Chiang Mai Hospital. These documents were annotated by physicians following the annotation guidelines based on international standards, including Systematized Nomenclature of Medicine-Clinical Terms (SNOMED-CT) and Health Level Seven Fast Healthcare Interoperability Resources (HL7 FHIR). The dataset was divided into fine-tuning (70 records, 78%, 2030 annotated spans), validation (10 records, 11%, 277 annotated spans), and testing sets (10 records, 11%, 358 annotated spans). The 5 transformer models were fine-tuned and evaluated using this annotated data to recognize and classify key medication entities (substance, route of administration, unit of measure, time patterns, and unit of presentation).

RESULTS: We found that all models had good NER performance metrics in both the validation and test datasets. Regarding test performance, ClinicalBERT achieved the highest exact F1-score at 0.973, compared with 0.968 for BioClinicalBERT, 0.925 for PubMedBERT, 0.931 for MultilingualBERT, and 0.969 for ThaiBERT. All models showed strength in accurately identifying “Substance” and “Dosage” entities, whereas “Unit of Measure” proved to be the most challenging entity type due to implicit information in the source text for all models.

CONCLUSIONS: The findings suggest that ontology-based medication IE using transformer-based models holds promise for enhancing data standardization and interoperability within the Thai health care system. Future work will need to leverage the granular annotations preserved in the dataset to develop medical concept normalization and relation extraction models to complete the medical IE system.

PMID:41861368 | DOI:10.2196/82685

J Med Internet Res. 2026 Mar 20;28:e78396. doi: 10.2196/78396.

ABSTRACT

BACKGROUND: Artificial intelligence (AI) has shown promise for automating spinal alignment assessment in adolescent idiopathic scoliosis (AIS). However, AI models typically exhibit reduced accuracy and robustness when deployed across multiple medical centers due to variability in imaging protocols and data characteristics, potentially compromising clinical diagnosis and treatment decisions.

OBJECTIVE: This study aimed to develop a real-time, plug-and-play data transformation method to enhance the robustness of deep learning models against data heterogeneity in radiographs, thereby improving their performance in assessing AIS across multiple medical centers.

METHODS: In this retrospective multicenter study, 3899 full-spine radiographs from 7 hospitals (2 from Hong Kong and 5 from Mainland China), collected between January 2012 and August 2024, were included. Data from 2 hospitals in Hong Kong (n=3034) were used for model training and internal validation, while radiographs from the 5 mainland hospitals (n=865) formed 5 independent external validation datasets. A novel pixel intensity-based data transformation method was developed to standardize image contrast and brightness across datasets and integrated into the model training process to enhance our previously developed AI model, SpineHRNet+. The enhanced model’s accuracy and robustness for cobb angle (CA) prediction and severity classification were evaluated using both internal and external datasets. Data heterogeneity across centers was quantified by brightness and contrast differences. CA prediction accuracy was evaluated using residual analysis, linear regression (coefficient of determination [R²]), and Bland-Altman analyses. Model performance for disease severity classification was assessed using sensitivity, specificity, precision, negative predictive value, accuracy, and confusion matrix analysis. The transformation method aligns pixel intensity distributions across datasets using statistical profiling and optimization, ensuring consistent image characteristics while preserving anatomical integrity.

RESULTS: The developed data transformation method significantly reduced contrast variability between datasets, improving consistency in image characteristics and enabling more reliable AI analysis. The enhanced SpineHRNet+ achieved consistent and accurate CA predictions across external validation datasets, with mean prediction errors within 4° (SD 3.12°), and maintained an R² greater than 0.90 for all centers. The sensitivity and negative predictive value for disease severity grading improved to 90.18% and 93.16%, respectively. Bland-Altman analyses demonstrated robust agreement, with 95% limits of agreement within 7.51° across all datasets.

CONCLUSIONS: The proposed data transformation approach effectively addressed data heterogeneity, significantly improving the accuracy and robustness of SpineHRNet+ in multicenter AIS assessments. The real-time processing capability and preservation of anatomical integrity underscore the method’s clinical practicality, enabling scalable and reliable AI applications in diverse health care environments.

PMID:41861366 | DOI:10.2196/78396

Prim Care Companion CNS Disord. 2026 Mar 10;28(2):25m04074. doi: 10.4088/PCC.25m04074.

ABSTRACT

Objective: To evaluate if ultra-low-dose mirtazapine (3.75 mg) improves insomnia without next-day effects.

Methods: This retrospective study evaluated data collected from September 5, 2024, to March 7, 2025, from an outpatient setting consisting of veterans with insomnia who were treated with ultra-low-dose mirtazapine. The Insomnia Severity Index (ISI) was administered during the first appointment and at each subsequent visit with the respective psychiatrist to monitor insomnia symptoms. Summary statistics were used to compare ISI scores at baseline and 1-3 months after starting treatment.

Results: Considering all veterans evaluated (N = 53), 47% showed a meaningful decrease in ISI value (greater than 7 points). Patients who completed treatment showed a constant or decreased ISI score (mean [SD] change: 11.3 [6.46]).

Conclusion: Ultra-low-dose mirtazapine may improve symptoms and ISI values for chronic insomnia.

Prim Care Companion CNS Disord 2026;28(2):25m04074.

Author affiliations are listed at the end of this article.

PMID:41861363 | DOI:10.4088/PCC.25m04074

Phys Rev Lett. 2026 Mar 6;136(9):094201. doi: 10.1103/7l2l-g5vn.

ABSTRACT

Atmospheric rivers (ARs) are essential components of the global hydrological cycle, with profound implications for water resources, extreme weather events, and climate dynamics. Yet, the statistical organization and underlying physical mechanisms of AR intensity and evolution remain poorly understood. Here we apply methods from statistical physics to analyze the full life cycle of ARs and identify universal signatures of self-organized criticality. We demonstrate that AR morphology exhibits nontrivial fractal geometry, while AR event sizes-quantified via integrated water vapor transport-follow robust power-law distributions, displaying finite-size scaling. To interpret these emergent behaviors, we develop a moisture avalanche model that reproduces the observed scaling laws and links them to threshold-driven moisture transport and precipitation dissipation. These scaling properties persist under warming scenarios, suggesting that ARs operate near a critical state as emergent, self-regulating systems. Concurrently, we observe a systematic poleward migration and intensification of ARs, driven by thermodynamic amplification and dynamical reorganization. Our findings establish a statistical physics framework for ARs, connecting critical phenomena to the spatiotemporal structure of extreme events in a warming climate.

PMID:41861341 | DOI:10.1103/7l2l-g5vn

Phys Rev Lett. 2026 Mar 6;136(9):096102. doi: 10.1103/crl6-wnfk.

ABSTRACT

We investigate the damage mechanisms governing crack propagation in disordered solids using random lattice models. Our simulations reveal two successive damage scaling laws at the crack tip, characterized by the coefficient of variation of the critical damage density. Close to the crack tip, the scaling exponent is ∼-0.5, transitioning to ∼-0.25 at larger distances. This transition uncovers a crack-tip equiprobable damage zone (EDZ), whose size increases with material disorder. Within the EDZ, damage exhibits statistical uniformity and the critical damage density is well approximated by a binomial distribution, reflecting the intrinsic stochasticity of fracture in disordered materials. These findings provide new physical insights into crack-tip damage and demonstrate how material disorder regulates crack propagation.

PMID:41861312 | DOI:10.1103/crl6-wnfk

Phys Rev Lett. 2026 Mar 6;136(9):091201. doi: 10.1103/fp1v-xrr5.

ABSTRACT

The amplification of magnetic fields is crucial for understanding the observed magnetization of stars and galaxies. Turbulent dynamo is the primary mechanism responsible for that but the understanding of its action in a collapsing environment is still rudimentary and relies on limited numerical experiments. We develop an analytical framework and perform numerical simulations to investigate the behavior of small-scale and large-scale dynamos in a collapsing turbulent cloud. This approach is also applicable to expanding environments and facilitates the application of standard dynamo theory to evolving systems. Using a supercomoving formulation of the magnetohydrodynamic equations, we demonstrate that dynamo action in a collapsing background leads to a superexponential growth of magnetic fields in time, significantly faster than the exponential growth seen in stationary turbulence. The enhancement is mainly due to the increasing eddy turnover rate during the collapse, which boosts the instantaneous growth rate of the dynamo. We also show that the scaling of final saturated magnetic field strength with density robustly exceeds the expectation from considerations of pure flux-freezing. Apart from establishing a formal framework for studying magnetic field evolution in collapsing (or expanding) turbulent plasmas, these findings suggest that during star and galaxy formation magnetic fields can become dynamically relevant much earlier than previously thought.

PMID:41861294 | DOI:10.1103/fp1v-xrr5

JCO Oncol Pract. 2026 Mar 20:OP2501121. doi: 10.1200/OP-25-01121. Online ahead of print.

ABSTRACT

PURPOSE: Patients overestimate their likelihood of benefit from early phase clinical trials (EPCT). Concerns about taking away hope from patients represent a purported barrier to prognostic discussions. In this study, we aimed to assess prognostic perceptions and hope among patients with advanced cancer at the time of enrollment in an EPCT.

METHODS: We enrolled patients at the time of EPCT enrollment at an academic medical center. Participants completed questionnaires assessing prognostic perceptions (Prognosis and Treatment Perceptions Questionnaire), hope (Herth Hope Index; range, 12-48, higher scores indicate higher hope), and symptoms (Edmonton Symptom Assessment System-Revised). We used descriptive statistics and regression models to explore associations of prognostic perceptions and hope.

RESULTS: Among 189 study participants (mean age = 62.5 years, 56.6% female, 93.1% White), 27.4% reported that the goal of their cancer treatment was to cure their cancer and 62.2% reported having conversations with their oncologist about prognosis. The majority (92.4%) reported that knowing about prognosis was extremely/very helpful. Patients’ mean hope score was 39.1 (standard deviation, 5.1). We found no association between hope and perceptions of the intent of cancer therapy (B = -0.59, P = .486) or hope and patient-reported frequency of conversations with their oncologist about prognosis (B = -1.25, P = .125).

CONCLUSION: At the time of enrollment in an EPCT, we found no association between patients’ hope and perception of the intent of cancer therapy or report of having discussed their prognosis with their oncologist. These findings suggest that patients can have hope despite acknowledging the noncurative intent of their therapy and prognostic discussions.

PMID:41861273 | DOI:10.1200/OP-25-01121

Medicine (Baltimore). 2026 Mar 20;105(12):e48005. doi: 10.1097/MD.0000000000048005.

ABSTRACT

This study aims to investigate the mediating role of the systemic immune-inflammation index (SII) in the relationship between obesity-related glycolipid indices and in vitro fertilization/intracytoplasmic sperm injection (IVF/ICSI) outcomes in women with polycystic ovary syndrome (PCOS). A total of 598 women diagnosed with PCOS according to the Rotterdam criteria and undergoing their first IVF/ICSI cycle at the Reproduction Medicine Center, Taizhou People’s Hospital Affiliated with Nanjing Medical University, Jiangsu, China between January 2021 and December 2023 were included. Key exposures included obesity-related metabolic indices (e.g., triglyceride to high-density lipoprotein ratio [TG/HDL], homeostasis model assessment of insulin resistance [HOMA-IR]) and the SII. The primary outcome was the live birth rate per initiated cycle. Associations were evaluated using multivariate generalized linear models, and causal mediation analysis was performed to quantify the proportion of the effect mediated by the SII. Higher TG/HDL, total cholesterol to HDL ratio (TC/HDL), low-density lipoprotein to HDL ratio (LDL/HDL), and HOMA-IR levels showed dose-dependent negative correlations with oocyte yield, fertilization rate, embryo quality, and live birth rate (all P < .05). An elevated SII was an independent predictor of a reduced live birth rate (β = -0.08, P = .008) and mediated 8.8% to 10.7% of the adverse effects of dyslipidemia (via TC/HDL and LDL/HDL) on live birth. This study shows that the SII is statistically linked to and potentially mediates the connection between metabolic dysfunction and poor IVF/ICSI outcomes in PCOS. Integrated strategies targeting both metabolism and inflammation may optimize fertility success in this population.

PMID:41861236 | DOI:10.1097/MD.0000000000048005

Medicine (Baltimore). 2026 Mar 20;105(12):e47960. doi: 10.1097/MD.0000000000047960.

ABSTRACT

There are three types of diabetes-related stigma (DRS): perceived, experienced, and internalized, all of which negatively impact individuals with diabetes. Over the past 2 decades, research in Japan has grown, highlighting the significant clinical effects of DRS. In this study, we focused on the least-studied form, experienced stigma investigating its prevalence, clinical correlates, and the awareness of DRS and advocacy activities among Japanese people with diabetes. We conducted a single-center, cross-sectional study from April 3 to 28, 2023, at the Ohta Nishinouchi Hospital Diabetes Center in Japan, involving 114 adults with type 1 or type 2 diabetes. Participants with severe mental or physical conditions were excluded. Each participant completed a questionnaire assessing experienced stigma, the impact of diabetes on their social life, and their familiarity with the terms “diabetes stigma” and “advocacy activities.” Associations between reported stigma and demographic or clinical factors were analyzed statistically. Our findings showed that only 19.3% of participants reported a significant impact of DRS on their social life, with younger individuals and those on multiple diabetes medications more likely to report experiencing stigma. Additionally, awareness of “diabetes stigma” and “advocacy activities” was notably low among participants. In conclusion, compared to international studies, the prevalence of experienced stigma among Japanese individuals with diabetes appears lower, based on this single-center face-to-face study of outpatients. However, age and polypharmacy were identified as significant factors associated with increased reports of stigma. Despite the limitations of a single-center design, small sample size, and use of non-validated survey tools, the observed low awareness of “diabetes stigma” and “advocacy activities” underscored the need for enhanced educational initiatives by healthcare professionals and diabetes-related organizations in Japan.

PMID:41861231 | DOI:10.1097/MD.0000000000047960