Category: Statistics

Arch Orthop Trauma Surg. 2026 May 2;146(1):171. doi: 10.1007/s00402-026-06325-0.

ABSTRACT

INTRODUCTION: Limb salvage centers have increased in number over time, but lack standardized defining criteria. This systematic review aimed to assess organizational features of limb salvage centers and determine whether orthoplastic centers, in comparison to vascular limb salvage centers, represent a distinct care model that may benefit from standardization.

METHODS: We conducted a systematic review of publications related to limb salvage centers by searching MEDLINE, Embase, Web of Science, and Cochrane databases from their inception through 2024. We quantified binary data extraction as a reporting score of 26 organizational features across six structural care domains for limb salvage centers, based on a validated quality measurement framework. Organizational features differentiating distinct center types were identified to establish a quality framework for orthoplastic centers. Statistical comparisons between center types were performed using appropriate tests (p < 0.05).

RESULTS: Of 118 included studies, orthoplastic (n = 43) and vascular (n = 48) centers represented 77% of all studies. Recent increases in orthoplastic publications show substantial variability in organizational features. Orthoplastic center literature more frequently reported plastic surgery consultation criteria (p < 0.001), surgical outcomes (p < 0.001), and centralized network integration (p ≤ 0.006), highlighting acute reconstructive approaches. Vascular center studies documented significantly more organizational team features (p < 0.001) and quality systems (p = 0.033), reflecting established care frameworks for chronic disease management. Six organizational features characterized orthoplastic centers with > 70% prevalence, providing a benchmark framework with standardization priorities.

CONCLUSION: Orthoplastic limb salvage centers demonstrate distinct care paradigms that benefit from standardization. Our findings suggest structural benchmarks to support the need for standardized development of orthoplastic limb salvage centers.

PMID:42069968 | DOI:10.1007/s00402-026-06325-0

Eur Radiol. 2026 May 2. doi: 10.1007/s00330-026-12577-6. Online ahead of print.

ABSTRACT

OBJECTIVES: To develop a semi-automated method to segment “black hole” lesions on post-gadolinium 2D T1-weighted images (GdT1) in multiple sclerosis (MS) that follows radiological intensity rules and perform multi-center validation.

MATERIALS AND METHODS: Multi-center spin-echo GdT1 images and accompanying proton-density (PD)/T2-weighted images and manual T2 lesion masks of the REFLEXION study (NCT00813709) of suspected/early MS were used. Briefly, the proposed method segments cortical gray matter (GM) to derive a T1-weighted intensity threshold, which is applied inside co-registered T2 lesion masks to segment black hole lesion voxels. It was optimized on a training set (N = 40, 57.5% female, mean age 31.4 ± 8.7 (standard deviation) years), and 274 patients formed the test set (61.3% female, age 31.8 ± 8.4 years). Performance was quantified by the Dice similarity coefficient (DSC) and the intraclass correlation coefficient (ICC) for absolute agreement with manual segmentations. Lesion-wise sensitivity and specificity were calculated.

RESULTS: Optimization resulted in: (1) GM selection as minimally 0.8 total WM plus GM partial volume, masked by MNI cortex; (2) normalized mutual information-driven linear co-registration of T2 to GdT1 images, interpolating T2 lesion masks using trilinear interpolation and 0.6 threshold; (3) mean intensity inside GM mask used as upper intensity threshold. The optimized method had acceptable spatial accuracy (DSC: 0.39 ± 0.26) and good volumetric accuracy (ICC: 0.84, 95% CI [0.72, 0.90]. Lesion-wise sensitivity was 0.91 ± 0.19, and lesion-wise specificity was 0.62 ± 0.22.

CONCLUSION: The proposed method to semi-automatically segment black holes from post-gadolinium T1-weighted images shows acceptable performance. As a potential aid to radiologists, the method is not recommended to be used entirely without human intervention.

KEY POINTS: Question T1-hypointense “black hole” lesions reflect disease severity in multiple sclerosis but are not routinely quantified due to a lack of reliable analysis methods. Findings A rule-based semi-automated method for GdT1 “black hole” lesion segmentation was developed and optimized, and then validated in a large unseen multi-center test set. Clinical relevance This method adds quantitative information about GdT1 “black hole” lesions to the radiological assessment of multiple sclerosis disease severity, when false positives are manually removed. This can enhance the characterization of individual patients and advance the understanding of the disease.

PMID:42069957 | DOI:10.1007/s00330-026-12577-6

Sci Rep. 2026 May 2. doi: 10.1038/s41598-026-50886-4. Online ahead of print.

ABSTRACT

Ranked Set Sampling (RSS) is known for its efficiency in parameter estimation, especially when ranking is more feasible than actual measurement. This study introduces a novel memory type estimator for RSS based on Hybrid Exponentially Weighted Moving Averages (HEWMA), using two auxiliary variables. The estimator aims to enhance efficiency by integrating current and past information along with secondary auxiliary data. Analytical expressions for the bias and mean square error (MSE) are derived, and the corresponding sample coefficients are obtained to simplify HEWMA weight calculations. A simulation study is conducted to evaluate the estimator under various conditions, including different sample sizes, distributional shapes (normal and skewed Weibull), and varying correlation levels among study and auxiliary variables. Results indicate that the proposed estimator consistently yields the highest relative efficiency (RE) compared to conventional and memory estimators using one or multiple auxiliary variables. Additionally, the estimator is applied to two real-world mortality datasets from the USA, involving deaths from tobacco- and alcohol-related cancers. Despite challenges such as zero values and small sample sizes, the estimator maintains superior performance. Overall, the proposed estimator offers an improvement in estimation performance for RSS, particularly in settings where auxiliary variables are available and memory type estimators are applicable.

PMID:42069952 | DOI:10.1038/s41598-026-50886-4

Sci Rep. 2026 May 2. doi: 10.1038/s41598-026-51535-6. Online ahead of print.

ABSTRACT

Federated learning (FL) has become a highly promising paradigm for privacy-preserving distributed model training by enabling edge devices to train without sharing raw data. But in practice, edge environments are both non-stationary and asymmetric, with varying data distributions due to shifts in user behaviour, sensing conditions, and overall environmental dynamics. This causes concept drift (sudden, gradual, and recurrent), leading to poor model performance, slower convergence, and predictive bias. Current approaches to FL are not combined to tackle problems of drift adaptation, differential privacy (DP) and resource efficiency (FedAvg, DP-FedAvg). To address these constraints, we present FedDriftGuard. This Federated learning layer unifies client-level drift detection, drift-adaptive aggregation, and adaptable differential privacy into a single, FLE architecture-compatible system. The proposed DP-DriftNet model implements attention-based time encoding to capture changing data patterns and drift-directed feature weighting to allow greater flexibility in the presence of distributional changes. A drift-optimal privacy scheduler allocates noise probabilistically, subject to a limited privacy budget, thereby enforcing an appropriate privacy-utility trade-off without cancelling formal DP guarantees. Also, update sparsification, compression and periodic transmission techniques are used to reduce communication overhead. Decades of experimentation on real-world and synthetic drift datasets have shown that FedDriftGuard outperforms baseline FL techniques, achieving accuracy and F1-score gains of 9-14% and 11-17%, respectively, with adaptation latency 28% shorter and communication cost 20-35% lower. Such findings are statistically significant and confirm the soundness of the suggested method. FedDriftGuard offers effective, scalable privacy-preserving learning in adaptable, edge-drifting environments.

PMID:42069950 | DOI:10.1038/s41598-026-51535-6

Sci Rep. 2026 May 2. doi: 10.1038/s41598-026-51652-2. Online ahead of print.

ABSTRACT

Zoonotic diseases are common threats to global health. A large number of infectious diseases are transmitted from animals to humans. The current study aimed to assess the community’s knowledge, attitudes, and practices (KAP) regarding common zoonotic diseases in the Arbaminch district. A cross-sectional survey was carried out between November 2024 and June 2025. A total of 384 participants were interviewed in the study. Participants residing in these areas were randomly chosen. Data were collected using a structured questionnaire. The collected data were analyzed using Stata 17, and the results were reported using descriptive statistics and the chi-square test. The findings of this study revealed that a majority (55%) of participants had good knowledge about zoonotic diseases. Respondents know several modes of transmission for zoonotic diseases, with animal bites (32.5%) being the most recognized, followed by direct contact (15.5%), ingestion of raw products (10%), and inhalation (10%). Regarding attitudes, 63.2% of respondents exhibited a positive attitude towards the importance of zoonotic disease prevention and control, and 67.4% of respondents followed relatively good hygiene and preventive behaviors. However, risky practices were still common. Knowledge score showed a significant association with age. Attitudes of participants were significantly associated with education, age, occupation, and income. Similarly, practices were significantly associated with gender, education level, occupation, and income, with all associations being statistically significant (p < 0.05). The overall community knowledge, attitudes, and practices regarding zoonotic diseases were relatively good.

PMID:42069944 | DOI:10.1038/s41598-026-51652-2

Sci Rep. 2026 May 2. doi: 10.1038/s41598-026-51605-9. Online ahead of print.

ABSTRACT

Oral lichen planus is a chronic disease of the oral mucosa, with pain as one of its main symptoms. This study aimed to assess the correlation between the results of four pain intensity measures-including the Visual Analog Scale (VAS), Numeric Rating Scale (NRS), Short Form McGill Pain Questionnaire (SF-MPQ), and Verbal Rating Scale (VRS)- in a population of patients diagnosed with oral lichen planus. In this prospective observational study, 66 patients with oral lichen planus participated. Four pain assessment scales were used, including VAS, NRS, VRS, and SF-MPQ. Participants completed these assessments at baseline and again after two weeks of treatment. A paired t-test, Spearman correlation, linear regression analysis, and adjusted multiple regression analysis (regarding age and level of education) were used to analyze the data. All four scales were sensitive to changes in pain after treatment and a significant reduction in pain scores was observed (p < 0.001). There was a strong positive correlation between all scales (p < 0.001). Regression analysis showed that scores on each scale could significantly predict scores on the other scales (p < 0.001). Multiple regression analysis adjusted for age and level of education, showed the correlations between the pain scales remained strong and significant (p < 0.001). These commonly used pain assessment scales showed strong correlation with each other, and it seems that the results obtained from each might be comparable with the others. However, further researches in larger studies and different populations are needed.

PMID:42069940 | DOI:10.1038/s41598-026-51605-9

Sci Rep. 2026 May 2. doi: 10.1038/s41598-026-50341-4. Online ahead of print.

ABSTRACT

Swallowing and diaphragmatic functions share neural regulatory pathways and require synchronous assessment. Patients who have had a stroke are susceptible to many complications, of which dysphagia and diaphragmatic dysfunction are particularly common. To compare the distribution and severity of swallowing function in stroke patients with and without diaphragmatic dysfunction, and to explore the correlation between swallowing and diaphragmatic functions. This cross-sectional observational study among 102 Chinese stroke patients with hemiplegia was conducted in August 2022 to December 2024. Data collection was completed in the first 48 h following admission, including sex, age, post-stroke duration, stroke type, stroke region, hemiplegia side, nasogastric feeding, and pneumonia. The patients were stratified into two groups by the presence or absence of diaphragmatic dysfunction, which was assessed by diaphragmatic ultrasound with a threshold of diaphragm thickening fraction (TFdi) < 20%. We compared the distribution and severity of different swallowing functions using the Modified Barium Swallow Study Impairment Profile (MBSImP) and the Penetration-Aspiration Scale (PAS) by Videofluoroscopic Swallowing Study (VFSS) between the two groups. Significant differences were found between the two groups in the oral and pharyngeal phases of the MBSImP (p < 0.003), including hold position/tongue control, bolus preparation/mastication, bolus transport/lingual motion, oral residue, initiation of the pharyngeal swallow, anterior hyoid motion, pharyngeal stripping wave, and pharyngeal residue (p < 0.003). In contrast, there were no significant differences between the two groups in some components of the MBSImP including lip closure, soft palate elevation, laryngeal elevation, epiglottic movement, laryngeal closure, pharyngeal contraction, and tongue base retraction (p > 0.003). The severity of swallowing physiological impairment by MBSImP between the two groups, including the oral phase, pharyngeal phase and total MBSImP scores showed significant differences (p < 0.003). By contrast, the distribution and severity of penetration and aspiration risk by PAS showed no statistically significant difference between the two groups (p > 0.003). TFdi was negatively correlated with grades of Water Swallowing Test, the oral phase, pharyngeal phase and total MBSImP scores (rs = -0.327 to -0.300, p < 0.003). Whereas no significant correlations were found between TFdi and pneumonia, nasogastric feeding and the PAS scores (p > 0.003). Patients with diaphragmatic dysfunction exhibited a higher proportion of swallowing physiological impairment in the oral and pharyngeal phases, along with greater severity of such impairments. Diaphragmatic function was correlated with swallowing function, but the correlation was weak and of uncertain clinical significance.

PMID:42069938 | DOI:10.1038/s41598-026-50341-4

NPJ Digit Med. 2026 May 2;9(1):347. doi: 10.1038/s41746-026-02662-x.

ABSTRACT

Access to large, diverse biomedical datasets is critical for advancing medical research, yet privacy regulations severely restrict data sharing. We present an end-to-end framework for privacy-preserving health data synthesis that integrates advanced deep generative models (DGMs) with robust preprocessing, formal differential privacy (DP) training for select DGMs, empirical privacy risk evaluation, data-sufficiency analysis, domain-guided quality control, and biobank visualization tools. Released as open-source containerized software, the framework ensures reproducible deployment while preserving statistical fidelity, machine learning (ML) utility, and privacy guarantees. Empirical evaluations across diverse biobank datasets demonstrate that TabSyn-a transformer-based diffusion model-combined with our correlation-and distribution-aware CorrDst loss function achieves superior performance balancing fidelity, privacy, and computational efficiency. The tailored preprocessing pipeline effectively handles high missingness rates, substantially improving distributional accuracy and clinical plausibility. Across 26 biobank datasets spanning three regulatory levels, the framework shows that TabSyn with correlation- and distribution-aware loss function consistently achieves superior performance in terms of fidelity, privacy, and computational efficiency.

PMID:42069937 | DOI:10.1038/s41746-026-02662-x

Sci Rep. 2026 May 2. doi: 10.1038/s41598-026-50398-1. Online ahead of print.

ABSTRACT

Early and accurate detection of plant leaf diseases is an essential requirement for precision agriculture, given their severe impact on global food security. While much has been done recently, many deep learning-based approaches will still fail in real-world tests because of challenges such as background clutter, differences in illumination, occlusion, or the fact that visual symptoms for these diseases can be very subtle early on. Traditional CNN- and Transformer-based architectures generally lack accurate lesion localisation and interpretability, hindering their practical deployment in agricultural decision-support tools. To address these issues, we present LDDHybridNet, a region-based, explanation-friendly deep learning framework that can identify leaf disease at an early, accurate stage. It then applies preprocessing steps guided by ROI, based on leaf segmentation from the U-Net, followed by a compact CNN-based spatial feature-extraction framework. We arrange spatial feature embeddings extracted from lesion regions into an ordered sequence and employ a Bi-LSTM with attention to model structured contextual dependencies, allowing progression-aware feature learning without requiring actual temporal image sequences. Lastly, Grad-CAM-based post-hoc explainability is employed to interpret model decisions, enabling transparent visualisation of disease-relevant regions. We conduct extensive experiments on the PlantVillage benchmark and the FieldPlant dataset and show that LDDHybridNet consistently outperforms representative CNN, transformer, and hybrid baselines across multiple evaluation metrics. Although the near-ceiling performance on PlantVillage reveals the dataset’s artificial nature, the proposed framework achieves 95.37% accuracy under real-world field conditions and 92.84% on weak-lesion early-stage samples, demonstrating the method’s robustness and early-stage detection potential. The performance boosts are statistically significant (P < 0.01). In general, LDDHybridNet is an interpretable and robust deep learning framework for leaf disease detection, which can support data-driven crop protection and precision agriculture applications.

PMID:42069934 | DOI:10.1038/s41598-026-50398-1

Arch Orthop Trauma Surg. 2026 May 2;146(1):168. doi: 10.1007/s00402-026-06328-x.

ABSTRACT

INTRODUCTION: The optimal fixation method in total hip arthroplasty (THA) remains under debate. While cemented fixation has been associated with a lower risk of periprosthetic fracture, uncemented fixation predominates in Japan. This study aimed to compare early postoperative complications between cemented and uncemented fixation in elective THA using a nationwide inpatient database.

MATERIALS AND METHODS: We identified 198,102 patients aged ≥ 65 years who underwent primary THA for osteoarthritis, osteonecrosis, or rheumatoid arthritis between December 2011 and March 2023 from the Japanese Diagnosis Procedure Combination (DPC) database. After 1:1 propensity score matching for age, sex, body mass index (BMI), and Charlson Comorbidity Index, 36,859 patients were included in each fixation cohort. Surgical and medical complications, and in-hospital mortality were compared using multivariate logistic regression.

RESULTS: Cemented fixation was associated with a significantly lower risk of periprosthetic fracture (odds ratio [OR], 0.40; 95% confidence interval [CI], 0.30-0.53; p < 0.001), blood transfusion (OR, 0.76; 95% CI, 0.74-0.78; p < 0.001), and deep vein thrombosis (OR, 0.79; 95% CI, 0.74-0.84; p < 0.001). There were no statistically significant differences based on the predefined threshold (p < 0.001) in dislocation, infection, pulmonary embolism, cardiac or cerebrovascular events, or in-hospital mortality between fixation types, although a trend toward higher in-hospital mortality in the cemented group was observed.

CONCLUSIONS: Cemented THA was associated with reduced rates of periprosthetic fracture, transfusion, and deep vein thrombosis without increasing other perioperative or medical complications. These findings suggest that cemented fixation may be associated with favorable short-term outcomes in selected patients.

PMID:42069928 | DOI:10.1007/s00402-026-06328-x