Category: Statistics

Contact Dermatitis. 2025 Dec 21. doi: 10.1111/cod.70073. Online ahead of print.

ABSTRACT

BACKGROUND: Chronic actinic dermatitis (CAD) is a photodermatosis associated with contact allergy. Changes in the contact allergen profile in patch-tested CAD patients from our department have been reported over several decades.

OBJECTIVES: To determine the frequency of positive patch tests and allergen profiles in recently investigated CAD patients and compare this to profiles in earlier decades.

METHODS: A retrospective cohort study was undertaken at a tertiary Cutaneous Allergy department between 2011 and 2021. Demographics and 10 allergens with highest positivity in CAD and non-CAD patients were compared.

RESULTS: Patch testing was performed in 309 (88.3%) of 349 CAD patients, with 186 (60.2%) testing positive to any allergen and 8 (2.6%) positive on photo-patch testing. Patients aged > 40 and with Fitzpatrick skin type V-VI were statistically more likely to be patch test positive (age > 40: p = 0.0082; Fitzpatrick skin type: p = 0.0361). Sesquiterpene lactones (SQL) (6.8%) and formaldehyde (4.8%) were amongst the top 10 most frequently positive allergens in CAD but not in non-CAD patients.

CONCLUSION: Allergic contact dermatitis remains prevalent amongst CAD patients, although sensitisation to allergens historically linked to CAD is decreasing. The cause of this is unclear but potentially due to changes in environmental exposures, particularly in younger CAD patients.

PMID:41423722 | DOI:10.1111/cod.70073

Med Phys. 2026 Jan;53(1):e70235. doi: 10.1002/mp.70235.

ABSTRACT

BACKGROUND: Accurate cardiac structure segmentation from echocardiography is essential for quantitative cardiac function assessment in clinical cardiology. However, traditional manual annotation is time-consuming and subjective, and existing automated methods often overlook inter-patient anatomical differences, particularly sex-related variability, limiting their generalizability.

PURPOSE: We propose BTY-Net (Bidirectional Encoder Representations from Transformers (BERT) Text-based Y-shaped Network), a novel automatic segmentation framework designed to incorporate patient-specific attributes, enabling personalized and anatomically adaptive segmentation of echocardiograms.

METHODS: BTY-Net is built upon a Unet3+ backbone combined with a Transformer encoder, incorporates a multi-layer denoising filter to enhance image quality, and employs a pre-trained BERT model to encode patient demographic and acquisition context as natural language embeddings. Experiments were conducted on the Cardiac Acquisitions for Multi-structure Ultrasound segmentation dataset (500 biplane cases, 400/50/50 train/validation/test split). We benchmarked eight state-of-the-art models (e.g., variants of Unet and generative adversarial networks). Dice similarity (Dice) and Hausdorff Distance (HD) served as the primary metrics. Statistical significance was assessed via the Wilcoxon signed-rank test with p < 0.05 as the threshold, and Holm-Bonferroni correction (α = 0.05) was applied for multiple comparisons. The Hedges’ g effect was calculated to quantify the difference.

RESULTS: BTY-Net achieved the highest Dice coefficients across the three cardiac structures (LV endocardium: 0.9316 ± 0.027; LV myocardium: 0.8617 ± 0.050; left atrium: 0.8703 ± 0.086) and the lowest HD values (8.14 ± 3.44, 10.97 ± 7.41, and 11.13 ± 8.16, respectively). Compared with the strongest baseline, BTY-Net improved Dice by up to 0.02-0.03 and reduced HD by approximately 1.1-1.5 mm, with Holm-adjusted p < 0.05 and small-to-medium Hedges’g effect sizes. Across all test cases, BTY-Net yielded the highest agreement with reference ejection fraction (correlation = 0.9119, MAE = 3.40%). Sex-stratified analyses further confirmed stable performance in both male and female subgroups, indicating robust adaptation to anatomical diversity.

CONCLUSIONS: BTY-Net offers an effective and interpretable solution for personalized echocardiographic analysis. By leveraging multimodal fusion of patient information and image data, it enhances segmentation accuracy, and embeds clinically meaningful attention maps, thereby delivering a multimodal, sex-robust and clinically interpretable solution for routine echocardiographic analysis.

PMID:41423714 | DOI:10.1002/mp.70235

Nutr Bull. 2025 Dec 21. doi: 10.1111/nbu.70040. Online ahead of print.

ABSTRACT

The double burden of malnutrition (DBM), defined as the coexistence of undernutrition and overnutrition within the same population or individual, is a growing concern in low- and middle-income countries (LMICs) such as Nepal. Identifying malnutrition in schools supports targeted interventions. This study estimates the prevalence of stunting, underweight, overweight and obesity among Nepalese schoolchildren and examines the coexistence of stunting and overweight. We conducted a cross-sectional study of 11 782 students aged 6-18 years from 111 randomly selected schools in Kaski, Nepal. Sociodemographic data were collected via questionnaire, and trained researchers measured height and weight. Malnutrition was classified using the World Health Organization (WHO) growth standards, and chi-square tests were used for statistical comparisons. The prevalence of stunting, underweight, overweight and obesity was 15.8%, 6.1%, 10.7% and 3.3%, respectively, while 1.0% of students were both stunted and overweight/obese. Stunting was more common in rural schools (20.5% vs. 15.1%, p < 0.001), whereas overweight (11.2% vs. 7.1%, p < 0.001) and obesity (3.6% vs. 1.5%, p < 0.001) were more common in urban schools. Public school students were more often stunted (18.2% vs. 13.2%, p < 0.004) and underweight (6.7% vs. 5.4%, p < 0.001), whereas private school students were more often overweight (13.3% vs. 8.2%, p < 0.001) and obese (5.0% vs. 1.7%, p < 0.001). The DBM occurs at both school and individual levels, including within the same school, with undernutrition more common in rural and public schools and overnutrition in urban and private schools. Tailored school-based nutrition programmes are urgently needed.

PMID:41423711 | DOI:10.1111/nbu.70040

J Cardiothorac Surg. 2025 Dec 21. doi: 10.1186/s13019-025-03773-4. Online ahead of print.

ABSTRACT

OBJECTIVE: To systematically review the clinical efficacy of amiodarone in the treatment of myocardial infarction in prehospital emergency.

METHODS: Articles related to the use of amiodarone in prehospital emergency treatment of myocardial infarction were retrieved from PubMed, Cochrane Library, EMBASE, Wanfang, VIP and CNKI databases, and the retrieval time was from the establishment of the database to October 31 2024. Meta-analysis and risk bias evaluation were carried out with R 4.2.2 software, and the results were considered statistically significant when P < 0.05.

RESULTS: A total of 16 studies involving 832 patients receiving prehospital amiodarone and 800 control patients were included in this systematic review and meta-analysis. Pooled results demonstrated that, compared to the control group, amiodarone significantly reduced the incidence of malignant arrhythmia (RR = 0.29, 95% CI: 0.22 to 0.37, P < 0.01), decreased the average number of defibrillations (MD = -2.40, 95% CI: -2.61 to -2.19, P < 0.01), improved the success rate of rescue (RR = 1.16, 95% CI: 1.12 to 1.21, P < 0.01), lowered the recurrence rate of myocardial infarction (RR = 0.22, 95% CI: 0.13 to 0.35, P < 0.01), reduced the incidence of adverse reactions (RR = 0.33, 95% CI: 0.12 to 0.87, P = 0.02), and shortened the length of hospital stay (MD = -3.81, 95% CI: -4.02 to -3.59, P < 0.01).

CONCLUSION: This systematic review and meta-analysis demonstrated that prehospital amiodarone administration in patients with myocardial infarction significantly reduced the incidence of malignant arrhythmia and the average number of defibrillations, while improving the success rate of rescue. Furthermore, it lowered the incidence of adverse reactions, the recurrence rate of myocardial infarction, and shortened the length of hospital stay. These findings support the clinical promotion of amiodarone in prehospital emergency care for myocardial infarction.

PMID:41423705 | DOI:10.1186/s13019-025-03773-4

Sci Rep. 2025 Dec 21. doi: 10.1038/s41598-025-29644-5. Online ahead of print.

ABSTRACT

The purpose of the current investigation is to design a novel radial basis neural network for solving the dynamical hepatitis C virus model in patients with a high baseline viral load, which represents the nonlinear dynamical structure. The infection and treatment in the hepatitis C virus comprise uninfected hepatocytes, creatively infected hepatocytes, and viruses. The aim of this study is to solve the dynamical hepatitis C virus model in patients with a high baseline viral load with the optimization of the Bayesian regularization scheme. A database reference solution is achieved by the explicit Runge-Kutta in interval 0 and 1 with the step size of 0.01 by data division into training as 72%, while 14%, 14% for endorsement, and testing. Twenty numbers of neurons, a feed forward neural network, activation radial basis function, and the optimization Bayesian regularization approach have been used to solve the hepatitis C virus model. The precision of the scheme is perceived by the outcomes overlapping and the reducible absolute error values, which are found as 10^-06 to 10^-08. A statistical evaluation utilizing various operators and proportional approaches is carried out in order to assess the solver’s efficiency.

PMID:41423703 | DOI:10.1038/s41598-025-29644-5

BMC Cancer. 2025 Dec 22. doi: 10.1186/s12885-025-15436-7. Online ahead of print.

ABSTRACT

OBJECTIVES: To evaluate the value of quantitative parameters and radiomic features based on dual-layer spectral detector CT (DLCT) in predicting spread through air spaces (STAS) of lung adenocarcinoma (LUAD).

METHODS: This study analyzed 266 patients with pathologically confirmed LUAD from two medical centers. Patients from center 1 were divided into training (n = 131) and internal validation (n = 57) sets, while center 2 (n = 78) formed the external validation set. Clinical data, conventional imaging features, and DLCT quantitative parameters were analyzed to develop a clinical-radiological model. Radiomic features were extracted from venous-phase images, including conventional images, virtual monoenergetic images (VMI) at 40 keV, 65 keV, and 100 keV, along with iodine density maps, effective atomic number (Zeff) maps, and electron density (ED) maps. The best-performing radiomics model was combined with clinical-radiological predictors to create a nomogram. Model performance was evaluated through ROC analysis, calibration curves, and decision curve analysis.

RESULTS: Multivariate analysis revealed that tumor-lung interface and ED values were independent predictive factors in the clinical-radiological model. The optimal radiomics model was constructed based on VMI 40 keV, demonstrating AUCs of 0.899, 0.835, and 0.828 in the training, internal validation, and external validation sets, respectively. The nomogram, which incorporated the VMI 40 keV radiomics signature along with tumor-lung interface and ED values, outperformed the clinical-radiological model in the training set (AUC = 0.910 vs. 0.870; P = 0.018) and the internal validation set (AUC = 0.868 vs. 0.798; P = 0.046). While the improvement in the external validation set was not statistically significant (AUC = 0.848 vs. 0.819; P = 0.184).

CONCLUSION: The nomogram, which integrates conventional imaging features, DLCT quantitative parameters and VMI 40 keV radiomic features, demonstrates promising performance and represents a potential valuable non-invasive tool for the preoperative assessment of STAS in LUAD.

PMID:41423690 | DOI:10.1186/s12885-025-15436-7

BMC Oral Health. 2025 Dec 22. doi: 10.1186/s12903-025-07526-6. Online ahead of print.

ABSTRACT

BACKGROUND: The complex and irregular morphology of prepared oval root canals poses optimal sealing in oval canals as a key endodontic challenge. To address this, this study utilized five types of standardized 3D-printed models replicating prepared anatomy of extracted teeth with oval canal, to characterize the morphological diversity of oval canals after preparation and evaluate the efficacy of three obturation techniques across different morphologies.

METHODS: Five standardized 3-dimensional printed models replicating prepared oval canal morphology were produced using cone-beam computed tomography (CBCT)-derived data from sixty extracted premolars, which had undergone standardized collection, screening, and in vitro preparation procedures. Three obturation techniques (n = 15 each) were evaluated: (1) lateral condensation with AH-Plus sealer (AHP-LC), (2) continuous wave vertical compaction with AH-Plus sealer (AHP-CWC), and (3) single-cone technique with iRoot SP sealer (SP-SC). Micro-CT scanning and volumetric analyses were employed to quantify the obturation quality. Statistical analysis was performed using Mann-Whitney U test, Kruskal-Wallis H test, Dunn’s test or Spearman rank’s correlation depending on the experimental design.

RESULTS: Micro-CT analysis revealed significant differences in the percentage of void volume (PVV) among the obturation techniques. Over the entire canal length, SP-SC demonstrated significantly lower PVV compared to AHP-LC and AHP-CWC (p < 0.01). In the apical third, canal morphology significantly influenced the PVV of AHP-CWC (p < 0.05), with a more circular cross-section (higher roundness) correlating strongly with a lower PVV (p < 0.001). No significant effects of morphology were observed in other canal segments or with the other obturation techniques.

CONCLUSIONS: SP-SC achieved more complete three-dimensional adaptation regardless of morphological variations in oval canals including varied diameter ratios, isthmus and recess. Conversely, AHP-CWC’s performance was significantly dependent on canal morphology in the apical third, where more circular cross-sections correlated with lower void volumes.

PMID:41423672 | DOI:10.1186/s12903-025-07526-6

Cancer Med. 2025 Dec;14(24):e71487. doi: 10.1002/cam4.71487.

ABSTRACT

BACKGROUND: Prostate cancer is among the most prevalent malignancies worldwide, and cardiovascular disease (CVD) is a major non-cancer cause of death in affected patients. Androgen deprivation therapy (ADT), a mainstay treatment, has raised concerns about cardiotoxicity, yet the CVD risks of individual ADT agents remain unclear.

OBJECTIVES: To assess cardiovascular adverse events (AEs) associated with specific ADT drugs using data from the U.S. FDA Adverse Event Reporting System (FAERS).

METHODS: AE reports related to ADT drugs were extracted from FAERS (Q1 2004-Q3 2024). Disproportionality analyses-including Reporting Odds Ratio (ROR) and Proportional Reporting Ratio (PRR)-were conducted to identify significant cardiovascular safety signals.

RESULTS: Different ADT agents exhibited distinct cardiovascular AE profiles. Some drugs were linked to a broader range of CVD-related AEs, while others had more limited associations.

CONCLUSIONS: ADT agents demonstrate heterogeneous cardiotoxicity profiles. These findings emphasize the need for individualized treatment strategies, particularly in patients with pre-existing CVD risks, and may aid clinicians in balancing cancer control with cardiovascular safety.

PMID:41423663 | DOI:10.1002/cam4.71487

Med Phys. 2026 Jan;53(1):e70206. doi: 10.1002/mp.70206.

ABSTRACT

BACKGROUND: Accurate and consistent image segmentation across longitudinal scans is essential in many clinical applications, including surveillance, treatment monitoring, and adaptive interventions. While personalized model adaptation using patient-specific prior scans has shown promise, current approaches typically rely on fixed training durations and lack mechanisms to determine optimal stopping points on a per-patient basis, particularly in the absence of validation labels.

PURPOSE: We propose an uncertainty-guided test-time optimization (TTO) framework that dynamically adjusts the personalization duration for each patient using a validation-free stopping criterion based on predictive uncertainty.

METHODS: Our framework personalizes a generalized segmentation model using patient-specific prior imaging and selects the optimal checkpoint based on the minimum voxel-wise predictive uncertainty, estimated via Monte Carlo Dropout (TTO-MCD) or Deep Ensembling (TTO-DE). We evaluated the approach on three datasets: 214 pancreas (CT) scans, 243 liver (CT) scans, and 175 head-and-neck tumor (MRI) scans, each containing a subset of patients with paired longitudinal scans to enable patient-specific personalization. Each patient’s follow-up scan was held out for testing. As a baseline, we implemented a fixed-epoch personalization strategy (Pre-TTO) using a fivefold cross-test design to emulate deployable model selection without test label leakage.

RESULTS: TTO methods consistently outperformed the Pre-TTO and unpersonalized baseline across standard metrics, including the Dice Similarity Coefficient (DSC), 95th percentile Hausdorff Distance (HD95), Mean Surface Distance (MSD), and the proposed LogPenalty Score (LPS), which provides a bounded, interpretable scale that jointly reflects volumetric and boundary fidelity. Paired t-tests confirmed statistically significant improvements for pancreas and liver datasets (p < 0.05), while favorable trends were observed in the head-and-neck dataset despite greater anatomical variability. Both TTO-MCD and TTO-DE achieved near-optimal performance without requiring access to labels at test time.

CONCLUSION: Uncertainty-guided TTO provides a robust, validation-free strategy for optimizing patient-specific segmentation models in longitudinal medical imaging. By tailoring personalization based on predictive uncertainty, our method improves segmentation quality across a range of imaging modalities and anatomical targets. This framework supports broad clinical deployment of personalized AI and motivates future extensions to contextual integration and multi-label segmentation. Code is publicly available at https://github.com/jchun-ai/uncertainty-tto.

PMID:41423658 | DOI:10.1002/mp.70206

BMC Nurs. 2025 Dec 21. doi: 10.1186/s12912-025-04240-0. Online ahead of print.

ABSTRACT

INTRODUCTION: Working conditions and occupational stress among nurses affect the quality of care and, consequently, patient satisfaction. Moreover, spiritual health among nurses is an essential component of healthcare services in achieving patient satisfaction. Therefore, the present study aimed to examine the relationship between nurses’ spiritual health and patients’ satisfaction with nursing services (PSNS) provided in southwest Iran.

METHODS: This descriptive-analytical, cross-sectional study was conducted among 80 nurses and their corresponding 80 patients in the clinical wards of Imam Khomeini and Razi Hospitals in Ahvaz in 2022. Data were collected using standardized questionnaires assessing spiritual health and PSNS, employing a convenience sampling method. Data analysis was performed using descriptive statistics, Spearman’s correlation test, and multiple linear regression in SPSS version 24. A significance level of p < 0.05 was considered.

RESULTS: The mean score of nurses’ spiritual health was 90.27 ± 15.32, and the mean score of PSNS was 97.23 ± 14.13, both at a moderate level. No significant association was found between spiritual health scores and demographic variables (p > 0.05). A significant association was observed between patient satisfaction scores and the frequency of hospital visits (p = 0.029). No statistically significant relationship was found between nurses’ spiritual health and patients’ satisfaction (p > 0.05). The coefficient of determination (R² = 0.175) indicated that the type of insurance, frequency of hospital visits, and marital status of patients together predicted 17.5% of the variance in patient satisfaction.

CONCLUSION: Spiritual health among nurses had no effect on patients’ satisfaction with nursing care, and no statistically significant relationship was found between these two variables. PSNS was influenced by their demographic characteristics and environmental factors such as frequency of visits, educational level, insurance coverage, and marital status, rather than by the nurses’ spiritual health. By improving service delivery processes, enhancing patients’ awareness of the treatment process, providing financial support for patients, and identifying other contributing factors, patients’ satisfaction can be improved.

PMID:41423647 | DOI:10.1186/s12912-025-04240-0