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Background: The "Key Points of the Core System of Medical Quality and Safety" (hereinafter referred to as the "Key Points") was promulgated by the National Health Commission of China in 2018, requiring that nursing ward rounds should be carried out with reference to the three-level ward round system; In 2020 and 2022 editions of the "Evaluation Standards for Tertiary Hospitals", which were implemented in China, required that nursing ward rounds should be carried out with reference to the "Key Points". Additionally, the Action Plan for Comprehensively Improving Medical Quality (2023-2025) also mentions the need to improve the quality of three-level ward rounds. However, there are no detailed guidelines regarding implementing "Nursing Three-level Ward Rounds". Purpose: This study aimed to investigate the current situation of nursing three-level ward rounds in tertiary hospitals after the promulgation of the "Key Points of the Core System of Medical Quality and Safety" to provide insights and guidelines regarding relevant standards, so as to better implement of the requirements of "nursing ward rounds" in the "Evaluation Standards for Tertiary Hospitals" and "improving the quality of three-level ward rounds" in the "Action Plan". Methods: A multi-center study was conducted in February 2024, including all tertiary public hospitals in the Shanxi Province, China. A questionnaire survey using the self-designed "Questionnaire on the Implementation of Nursing Three-level Ward Rounds" was carried out. The questionnaire included the basic information of the hospital and the implementation of the three-level (namely I, II, and III) rounds (including "five aspects": ward round personnel, object, content, frequency, and record), which is expressed by quantity and composition ratio. Next is the text analysis method. First, the "five aspects" of the hospital that filled in the questionnaire survey with "nursing three-level ward rounds have been carried out" were assessed. Second, the five aspects of each hospital were assessed for consistency with the "Nursing Three-level Ward Rounds System" (hereinafter referred to as the "System") of their respective hospitals.Third, the consistency of the "System" of the hospital with the "Key Points" was assessed. The results of the analysis of the former are expressed in terms of quantity and composition ratio; the results of the latter two were analyzed using Fisher's exact test method to compare any differences. Results: Notably, 14 of the 67 tertiary public hospitals (20.9%) carried out nursing three-level ward rounds. There were 4-10 situations in the five aspects of I, II, and III ward rounds filled in by the hospitals. The five aspects of the I, II, III ward rounds in 14 hospitals were significantly comparable with the "System", which, in turn, was comparable with the "Key Points" (P < 0.05). Conclusion: Not all tertiary public hospitals in the Shanxi Province have not all carried out nursing three-level ward rounds. Furthermore, the five aspects of the hospitals that carried out nursing three-level ward rounds were not entirely consistent in terms of ward round personnel, object, content, frequency, and record. The filling in of the nursing three-level ward rounds carried out by the hospitals is inconsistent with the respective "System"; the "System" of the hospital is not in line with the "Key Points". Impact on Nursing Work: Nursing administrators should be aware of the newly issued norms and requirements in their workplace, and revise the relevant systems in accordance with the norms and requirements in a timely manner. Additionally, the revision of the system should cover the core requirements of the norms and be practicable. The system should be supervised to ensure that 100% of the implementation is in accordance with the system.

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Background: Diminished ovarian reserve (DOR) refers to a decrease in the number or quality of oocytes in the ovarian cortex, which is a degenerative disease of the reproductive system, and can further develop into premature ovarian failure. There are few studies on acupuncture and moxibustion for DOR, which are still in the exploratory stage. Methods/design: This study was a real-world case registry study. According to whether the subjects received conception vessel acupuncture or not, they were divided into the basic treatment combined with conception vessel acupuncture group and the basic treatment group. A total of 1221 patients with DOR were enrolled and treated for 12 weeks. The percentage of patients with ≥30% improvement in anti-Müllerian hormone (AMH) was evaluated at the end of week 12. Secondary outcomes included Antral follicle count (AFC), modified Kupperman scale, basal FSH level, LH level, FSH/LH ratio, positive pregnancy, clinical pregnancy, early spontaneous abortion, ongoing pregnancy, and ectopic pregnancy. Discussion: This study provides clinical evidence and theoretical support for the treatment of DOR with conception vessel acupuncture and moxibustion, so as to guide and improve the efficacy of acupuncture and moxibustion. Trial registration: Acupuncture-Moxibustion Clinical Trial Registry ChiCTR2400080471. Registered on 30 January 2024.

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Background: Currently, studies found that the humanistic care ability of nurses is at low level in China, resulting in patients' concerns and dissatisfaction regarding the lack of empathy among nurses. We aimed to explore the factors that influence nurses' humanistic care ability, providing a new perspective on improving patient satisfaction and promote high quality medical services. Methods: A multi-center cross-sectional study recruited nurses from tertiary and secondary hospitals in China between July 2022 and August 2022. Data concerning self-developed questions on nurses' socio-demographic data and Caring Ability Inventory (CAI) were collected through the Questionnaire Star Platform, using a multi-stage sampling method. Results: The total score for the level of caring ability among the 15,653 surveyed Chinese nurses was 192.16 ± 24.94. Various factors significantly influence the level of humanistic care ability, including professional title, department, degree of passion for the job, job satisfaction, emphasis on self-care, participation in humanistic care training, support from family for the job, relationships with colleagues, satisfaction with salary, and previous experience working in pilot wards emphasizing humanistic care (p < 0.01). Conclusion: At present, nurses exhibit a comparatively modest proficiency in humanistic care ability. Numerous factors contribute to this situation. Nursing administrators ought to enhance the scope of humanistic care practices, conduct consistent professional training sessions, advocate for the implementation of model wards emphasizing humanistic care, foster a supportive organizational culture conducive to nurses, and underscore the significance of both nurturing nurses and promoting self-care among them.

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ComBat harmonization has been developed to remove non-biological variations for data in multi-center research applying artificial intelligence (AI). We investigated the effectiveness of ComBat harmonization on radiomic and deep features extracted from large, multi-center abdominal MRI data. A retrospective study was conducted on T2-weighted (T2W) abdominal MRI data retrieved from individual patients with suspected or known chronic liver disease at three study sites. MRI data were acquired using systems from three manufacturers and two field strengths. Radiomic features and deep features were extracted using the PyRadiomics pipeline and a Swin Transformer. ComBat was used to harmonize radiomic and deep features across different manufacturers and field strengths. Student's t-test, ANOVA test, and Cohen's F score were applied to assess the difference in individual features before and after ComBat harmonization. Between two field strengths, 76.7%, 52.9%, and 26.7% of radiomic features, and 89.0%, 56.5%, and 0.1% of deep features from three manufacturers were significantly different. Among the three manufacturers, 90.1% and 75.0% of radiomic features and 89.3% and 84.1% of deep features from two field strengths were significantly different. After ComBat harmonization, there were no significant differences in radiomic and deep features among manufacturers or field strengths based on t-tests or ANOVA tests. Reduced Cohen's F scores were consistently observed after ComBat harmonization. ComBat harmonization effectively harmonizes radiomic and deep features by removing the non-biological variations due to system manufacturers and/or field strengths in large multi-center clinical abdominal MRI datasets.

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INTRODUCTION: The aim of this work is to develop a deep learning (DL) system for rapidly and accurately screening for intraocular tumor (IOT), retinal detachment (RD), vitreous hemorrhage (VH), and posterior scleral staphyloma (PSS) using ocular B-scan ultrasound images. METHODS: Ultrasound images from five clinically confirmed categories, including vitreous hemorrhage, retinal detachment, intraocular tumor, posterior scleral staphyloma, and normal eyes, were used to develop and evaluate a fine-grained classification system (the Dual-Path Lesion Attention Network, DPLA-Net). Images were derived from five centers scanned by different sonographers and divided into training, validation, and test sets in a ratio of 7:1:2. Two senior ophthalmologists and four junior ophthalmologists were recruited to evaluate the system's performance. RESULTS: This multi-center cross-sectional study was conducted in six hospitals in China. A total of 6054 ultrasound images were collected; 4758 images were used for the training and validation of the system, and 1296 images were used as a testing set. DPLA-Net achieved a mean accuracy of 0.943 in the testing set, and the area under the curve was 0.988 for IOT, 0.997 for RD, 0.994 for PSS, 0.988 for VH, and 0.993 for normal. With the help of DPLA-Net, the accuracy of the four junior ophthalmologists improved from 0.696 (95% confidence interval [CI] 0.684-0.707) to 0.919 (95% CI 0.912-0.926, p < 0.001), and the time used for classifying each image reduced from 16.84 ± 2.34 s to 10.09 ± 1.79 s. CONCLUSIONS: The proposed DPLA-Net showed high accuracy for screening and classifying multiple ophthalmic diseases using B-scan ultrasound images across mutiple centers. Moreover, the system can promote the efficiency of classification by ophthalmologists.

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BACKGROUND: Digital subtraction angiography (DSA) devices are commonly used in numerous interventional procedures across various parts of the body, necessitating multiple scans per procedure, which results in significant radiation exposure for both doctors and patients. Inspired by generative artificial intelligence techniques, this study proposes GenDSA, a large-scale pretrained multi-frame generative model-based real-time and low-dose DSA imaging system. METHODS: GenDSA was developed to generate 1-, 2-, and 3-frame sequences following each real frame. A large-scale dataset comprising ∼3 million DSA images from 27,117 patients across 10 hospitals was constructed to pretrain, fine-tune, and validate GenDSA. Two other datasets from 25 hospitals were used for evaluation. Objective evaluations included SSIM and PSNR. Five interventional radiologists independently assessed the quality of the generated frames using the Likert scale and visual Turing test. Scoring consistency among the radiologists was measured using the Kendall coefficient of concordance (W). The Fleiss' kappa values were used for inter-rater agreement analysis for visual Turing tests. FINDINGS: Using only one-third of the clinical radiation dose, videos generated by GenDSA were perfectly consistent with real videos. Objective evaluations demonstrated that GenDSA's performance (PSNR = 36.83, SSIM = 0.911, generation time = 0.07 s/frame) surpassed state-of-the-art algorithms. Subjective ratings and statistical results from five doctors indicated no significant difference between real and generated videos. Furthermore, the generated videos were comparable to real videos in overall quality (4.905 vs. 4.935) and lesion assessment (4.825 vs. 4.860). CONCLUSIONS: With clear clinical and translational values, the developed GenDSA can significantly reduce radiation damage to both doctors and patients during DSA-guided procedures. FUNDING: This study was supported by the National Key R&D Program and the National Natural Science Foundation of China.

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BACKGROUND: Given the geographical sparsity of Rare Diseases (RDs), assembling a cohort is often a challenging task. Common data models (CDM) can harmonize disparate sources of data that can be the basis of decision support systems and artificial intelligence-based studies, leading to new insights in the field. This work is sought to support the design of large-scale multi-center studies for rare diseases. METHODS: In an interdisciplinary group, we derived a list of elements of RDs in three medical domains (endocrinology, gastroenterology, and pneumonology) according to specialist knowledge and clinical guidelines in an iterative process. We then defined a RDs data structure that matched all our data elements and built Extract, Transform, Load (ETL) processes to transfer the structure to a joint CDM. To ensure interoperability of our developed CDM and its subsequent usage for further RDs domains, we ultimately mapped it to Observational Medical Outcomes Partnership (OMOP) CDM. We then included a fourth domain, hematology, as a proof-of-concept and mapped an acute myeloid leukemia (AML) dataset to the developed CDM. RESULTS: We have developed an OMOP-based rare diseases common data model (RD-CDM) using data elements from the three domains (endocrinology, gastroenterology, and pneumonology) and tested the CDM using data from the hematology domain. The total study cohort included 61,697 patients. After aligning our modules with those of Medical Informatics Initiative (MII) Core Dataset (CDS) modules, we leveraged its ETL process. This facilitated the seamless transfer of demographic information, diagnoses, procedures, laboratory results, and medication modules from our RD-CDM to the OMOP. For the phenotypes and genotypes, we developed a second ETL process. We finally derived lessons learned for customizing our RD-CDM for different RDs. DISCUSSION: This work can serve as a blueprint for other domains as its modularized structure could be extended towards novel data types. An interdisciplinary group of stakeholders that are actively supporting the project's progress is necessary to reach a comprehensive CDM. CONCLUSION: The customized data structure related to our RD-CDM can be used to perform multi-center studies to test data-driven hypotheses on a larger scale and take advantage of the analytical tools offered by the OHDSI community.

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Automatic deidentification of Electronic Health Records (EHR) is a crucial step in secondary usage for biomedical research. This study introduces evaluation of an intricate hybrid deidentification strategy to enhance patient privacy in secondary usage of EHR. Specifically, this study focuses on assessing automatic deidentification using OpenDeID pipeline across diverse corpora for safeguarding sensitive information within EHR datasets by incorporating diverse corpora. Three distinct corpora were utilized: the OpenDeID v2 corpus containing pathology reports from Australian hospitals, the 2014 i2b2/UTHealth deidentification corpus with clinical narratives from the USA, and the 2016 CEGS N-GRID identification corpus comprising psychiatric notes. The OpenDeID pipeline employs a hybrid approach based on deep learning and contextual rules. Pre-processing steps involved harmonizing and addressing encoding and format issues. Precision, Recall, F-measure metrics were used to assess the performance. The evaluation metrics demonstrated the superior performance of the Discharge Summary BioBERT model. Trained on three corpora with a total of 4,038 reports, the best performing model exhibited robust deidentification capabilities when applied to EHR. It achieved impressive micro-averaged F1-scores of 0.9248 and 0.9692 for strict and relaxed settings, respectively. These results offer valuable insights into the model's efficacy and its potential role in safeguarding patient privacy in secondary usage of EHR.

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BACKGROUND AND PURPOSE: In recent years, new systemic therapies have been developed for hepatocellular carcinoma (HCC). The aim of this study was to evaluate the prognosis of patients with unresectable HCC treated with R0 hepatectomy after systemic therapy. METHODS: Data from 27 patients who underwent hepatectomy for HCC after systemic therapy at six facilities were analyzed retrospectively. Cancer-specific survival (CSS) and recurrence-free survival (RFS) after hepatectomy were investigated using Kaplan-Meier curves. We examined the prognostic value of the oncological criteria of resectability for HCC reported by the Japanese Expert Consensus 2023. RESULTS: R0 resection was performed in 24 of the 27 patients. Using the Response Evaluation Criteria in Solid Tumors, 0 patient had a complete response, 16 had a partial response, 6 had stable disease, and 2 had progressive disease. Median CSS was not evaluated, but the median RFS was 17.8 months. Patients with resectable and borderline resectable (BR) 1 cancers had a better prognosis than those with BR2 cancers. The group whose oncological criteria were improved by systemic therapy had a lower recurrence rate than the group whose oncological criteria were maintained, but no difference was observed in CSS. CONCLUSIONS: The findings of this study suggest that hepatectomy after systemic therapy may improve the prognosis of HCC patients.

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Diabetic peripheral neuropathy is one of the diabetes most common microvascular complications. It is very prevalent in Sub-Saharan Africa due to a combination of causes, including rising diabetes prevalence, limited healthcare resources, and a lack of access to competent medical care. However, just a few studies have been undertaken in the study area. Institution-based cross-sectional study was conducted in the Amhara region referral hospitals, in 2022. By using a systematic random sampling technique, a total of 627 respondents were included. The data was entered into EPI Data version 4.6 and exported to SPSS version 25 for further analysis. A binary logistic regression was used to determine the relationship between the dependent and predictor variables. The association between predictor variables and the dependent variable was determined using multivariate logistic regression [p value < 0.05, 95% confidence interval]. The overall prevalence of diabetic peripheral neuropathy among the study participants was 48.2% (95% CI; 44.2, 52.1). Aged between 40 and 60 years (AOR = 4:27; 95% CI 2.62, 6.94), and 60 years and older (AOR = 4:47; 95% CI 2.40, 8.35), participants who have lived alone (AOR = 2:14; 95% CI 1.21, 3.79), patients with comorbidity (AOR = 1:83; 95% CI 1.22, 2.76), and being physically inactive (AOR = 1:69; 95% CI 1.14, 2.49) were significantly associated with Diabetic peripheral neuropathy. Diabetic peripheral neuropathy was high among diabetic patients. Healthcare providers should prioritize regular screening and early intervention for individuals at higher risk, particularly those aged 40 and above, those living alone, patients with comorbid conditions, and those who are physically inactive. Implementing community-based support programs, encouraging physical activity, and providing comprehensive management plans for diabetes and associated comorbidities can help mitigate the risk and improve the quality of life for these patients.

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BACKGROUND AND PURPOSE: To investigate the feasibility of synthesizing computed tomography (CT) images from magnetic resonance (MR) images in multi-center datasets using generative adversarial networks (GANs) for rectal cancer MR-only radiotherapy. MATERIALS AND METHODS: Conventional T2-weighted MR and CT images were acquired from 90 rectal cancer patients at Peking University People's Hospital and 19 patients in public datasets. This study proposed a new model combining contrastive learning loss and consistency regularization loss to enhance the generalization of model for multi-center pelvic MRI-to-CT synthesis. The CT-to-sCT image similarity was evaluated by computing the mean absolute error (MAE), peak signal-to-noise ratio (SNRpeak), structural similarity index (SSIM) and Generalization Performance (GP). The dosimetric accuracy of synthetic CT was verified against CT-based dose distributions for the photon plan. Relative dose differences in the planning target volume and organs at risk were computed. RESULTS: Our model presented excellent generalization with a GP of 0.911 on unseen datasets and outperformed the plain CycleGAN, where MAE decreased from 47.129 to 42.344, SNRpeak improved from 25.167 to 26.979, SSIM increased from 0.978 to 0.992. The dosimetric analysis demonstrated that most of the relative differences in dose and volume histogram (DVH) indicators between synthetic CT and real CT were less than 1%. CONCLUSION: The proposed model can generate accurate synthetic CT in multi-center datasets from T2w-MR images. Most dosimetric differences were within clinically acceptable criteria for photon radiotherapy, demonstrating the feasibility of an MRI-only workflow for patients with rectal cancer.

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Cox regression and Kaplan-Meier estimations are often needed in clinical research and this requires access to individual patient data (IPD). However, IPD cannot always be shared because of privacy or proprietary restrictions, which complicates the making of such estimations. We propose a method that generates pseudodata replacing the IPD by only sharing non-disclosive aggregates such as IPD marginal moments and a correlation matrix. Such aggregates are collected by a central computer and input as parameters to a Gaussian copula (GC) that generates the pseudodata. Survival inferences are computed on the pseudodata as if it were the IPD. Using practical examples we demonstrate the utility of the method, via the amount of IPD inferential content recoverable by the GC. We compare GC to a summary-based meta-analysis and an IPD bootstrap distributed across several centers. Other pseudodata approaches are also considered. In the empirical results, GC approximates the utility of the IPD bootstrap although it might yield more conservative inferences and it might have limitations in subgroup analyses. Overall, GC avoids many legal problems related to IPD privacy or property while enabling approximation of common IPD survival analyses otherwise difficult to conduct. Sharing more IPD aggregates than is currently practiced could facilitate "second purpose"-research and relax concerns regarding IPD access.

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AIM: To evaluate the effects of lipid-lowering medications of different intensities on total, calcified, and non-calcified plaque volumes in patients undergoing serial cardiac computed tomography angiography (CCTA). METHODS: Individuals with chronic coronary syndromes from 11 centers were included in a retrospective registry. Total, calcified, and non-calcified plaque volumes were quantified and the relative difference in plaque volumes between baseline and follow-up CCTA was calculated. The intensity of lipid-lowering treatment was designated as low, moderate, or high, based on current recommendations. RESULTS: Of 216 patients (mean age 63.1 ± 9.7 years), undergoing serial CCTA (median timespan = 824.5 [IQR = 463.0-1323.0] days), 89 (41.2%) received no or low-intensity lipid-lowering medications, and 80 (37.0%) and 47 (21.8%) moderate- and high-intensity lipid-lowering agents, respectively. Progression of total and non-calcified plaque was attenuated in patients on moderate-/high- versus those on no/low-intensity treatment and arrested in patients treated with high-intensity statins or PCSK9 inhibitors (p < 0.001). Halted increase of non-calcified plaque was associated with LDL-cholesterol reduction (p < 0.001), whereas calcified plaque mass and Agatston score increased irrespective of the lipid-lowering treatment (p = NS). The intensity of lipid-lowering therapy robustly predicted attenuation of non-calcified plaque progression as a function of the time duration between the two CCTA scans, and this was independent of age and cardiovascular risk factors (HR = 3.83, 95% CI = 1.81-8.05, p < 0.001). CONCLUSION: The LOCATE multi-center observational study shows that progression of non-calcified plaques, which have been previously described as precursors of acute coronary syndromes, can be attenuated with moderate-intensity, and arrested with high-intensity lipid-lowering therapy. GERMAN CLINICAL TRIALS REGISTER: DRKS00031954.

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Background: The red blood cell distribution width (RDW) is closely linked to the prognosis of multiple diseases. However, the connection between RDW and gastrointestinal bleeding (GIB) in stroke patients is not well understood. This study aimed to clarify this association. Methods: This retrospective study involved 11,107 hospitalized patients from 208 hospitals in the United States, admitted between January 1, 2014, and December 31, 2015. We examined clinical data from 7,512 stroke patients in the intensive care unit (ICU). Multivariate logistic regression assessed the link between RDW and in-hospital GIB in stroke patients. Generalized additive model (GAM) and smooth curve fitting (penalty spline method) were utilized to explore the non-linear relationship between RDW and GIB in stroke patients. The inflection point was calculated using a recursive algorithm, and interactions between different variables were assessed through subgroup analyses. Results: Among the 11,107 screened stroke patients, 7,512 were included in the primary analysis, with 190 identified as having GIB. The participants had a mean age of (61.67 ± 12.42) years, and a median RDW of 13.9%. Multiple logistic analysis revealed RDW as a risk factor for in-hospital GIB in stroke patients (OR = 1.28, 95% CI 1.21, 1.36, p < 0.05). The relationship between RDW and in-hospital GIB in stroke patients was found to be non-linear. Additionally, the inflection point of RDW was 14.0%. When RDW was ≥14.0%, there was a positive association with the risk of GIB (OR: 1.24, 95% CI: 1.16, 1.33, p < 0.0001). Conversely, when RDW was <14.0%, this association was not significant (OR: 1.02, 95% CI: 0.97-1.07, p = 0.4040). Conclusion: This study showed a substantial non-linear link between RDW and the risk of GIB in stroke patients. Maintaining the patient's RDW value below 14.0% could lower the risk of in-hospital GIB.

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Purpose: Fundus fluorescein angiography (FFA) is the gold standard for retinal vein occlusion (RVO) diagnosis. This study aims to develop a deep learning-based system to diagnose and classify RVO using FFA images, addressing the challenges of time-consuming and variable interpretations by ophthalmologists. Methods: 4028 FFA images of 467 eyes from 463 patients were collected and annotated. Three convolutional neural networks (CNN) models (ResNet50, VGG19, InceptionV3) were trained to generate the label of image quality, eye, location, phase, lesions, diagnosis, and macular involvement. The performance of the models was evaluated by accuracy, precision, recall, F-1 score, the area under the curve, confusion matrix, human-machine comparison, and Clinical validation on three external data sets. Results: The InceptionV3 model outperformed ResNet50 and VGG19 in labeling and interpreting FFA images for RVO diagnosis, achieving 77.63%-96.45% accuracy for basic information labels and 81.72%-96.45% for RVO-relevant labels. The comparison between the best CNN and ophthalmologists showed up to 19% accuracy improvement with the inceptionV3. Conclusion: This study developed a deep learning model capable of automatically multi-label and multi-classification of FFA images for RVO diagnosis. The proposed system is anticipated to serve as a new tool for diagnosing RVO in places short of medical resources.

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OBJECTIVES: This study investigated the use of radiomics for diagnosing early-stage osteonecrosis of the femoral head (ONFH) by extracting features from multiple MRI sequences and constructing predictive models. MATERIALS AND METHODS: We conducted a retrospective review, collected MR images of early-stage ONFH (102 from institution A and 20 from institution B) and healthy femoral heads (102 from institution A and 20 from institution B) from two institutions. We extracted radiomics features, handled batch effects using Combat, and normalized features using z-score. We employed the Least absolute shrinkage and selection operator (LASSO) algorithm, along with Max-Relevance and Min-Redundancy (mRMR), to select optimal features for constructing radiomics models based on single, double, and multi-sequence MRI data. We evaluated performance using receiver operating characteristic (ROC) and precision-recall (PR) curves, and compared area under curve of ROC (AUC-ROC) values with the DeLong test. Additionally, we studied the diagnostic performance of the multi-sequence radiomics model and radiologists, compared the diagnostic outcomes of the model and radiologists using the Fisher exact test. RESULTS: We studied 122 early-stage ONFH and 122 normal femoral heads. The multi-sequence model exhibited the best diagnostic performance among all models (AUC-ROC, PR-AUC for training set: 0.96, 0.961; validation set: 0.96, 0.97; test set: 0.94, 0.94), and it outperformed three resident radiologists on the external testing group with an accuracy of 87.5 %, sensitivity of 85.00 %, and specificity of 90.00 % (p < 0.01), highlighting the robustness of our findings. CONCLUSIONS: Our study underscored the novelty of the multi-sequence radiomics model in diagnosing early-stage ONFH. By leveraging features extracted from multiple imaging sequences, this approach demonstrated high efficacy, indicating its potential to advance early diagnosis for ONFH. These findings provided important guidance for enhancing early diagnosis of ONFH through radiomics methods, offering new avenues and possibilities for clinical practice and patient care.

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Multi-center disease diagnosis aims to build a global model for all involved medical centers. Due to privacy concerns, it is infeasible to collect data from multiple centers for training (i.e., centralized learning). Federated Learning (FL) is a decentralized framework that enables multiple clients (e.g., medical centers) to collaboratively train a global model while retaining patient data locally for privacy. However, in practice, the data across medical centers are not independently and identically distributed (Non-IID), causing two challenging issues: (1) catastrophic forgetting at clients, i.e., the local model at clients will forget the knowledge received from the global model after local training, causing reduced performance; and (2) invalid aggregation at the server, i.e., the global model at the server may not be favorable to some clients after model aggregation, resulting in a slow convergence rate. To mitigate these issues, an innovative Federated learning using Model Projection (FedMoP) is proposed, which guarantees: (1) the loss of local model on global data does not increase after local training without accessing the global data so that the performance will not be degenerated; and (2) the loss of global model on local data does not increase after aggregation without accessing local data so that convergence rate can be improved. Extensive experimental results show that our FedMoP outperforms state-of-the-art FL methods in terms of accuracy, convergence rate and communication cost. In particular, our FedMoP also achieves comparable or even higher accuracy than centralized learning. Thus, our FedMoP can ensure privacy protection while outperforming centralized learning in accuracy and communication cost.

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BACKGROUND & AIMS: Oral antiviral therapy with nucleos(t)ide analogues (NAs) for chronic hepatitis B (CHB) is well-tolerated and lifesaving, but real-world data on utilization are limited. We examined rates of evaluation and treatment in patients from the REAL-B consortium. METHODS: This was a cross-sectional study nested within our retrospective multinational clinical consortium (2000-2021). We determined the proportions of patients receiving adequate evaluation, meeting AASLD treatment criteria, and initiating treatment at any time during the study period. We also identified factors associated with receiving adequate evaluation and treatment using multivariable logistic regression analyses. RESULTS: We analyzed 12,566 adult treatment-naïve patients with CHB from 25 centers in 9 countries (mean age 47.1 years, 41.7% female, 96.1% Asian, 49.6% Western region, 8.7% cirrhosis). Overall, 73.3% (9,206 patients) received adequate evaluation. Among the adequately evaluated, 32.6% (3,001 patients) were treatment eligible by AASLD criteria, 83.3% (2,500 patients) of whom were initiated on NAs, with consistent findings in analyses using EASL criteria. On multivariable logistic regression adjusting for age, sex, cirrhosis, and ethnicity plus region, female sex was associated with adequate evaluation (adjusted odds ratio [aOR] 1.13, p = 0.004), but female treatment-eligible patients were about 50% less likely to initiate NAs (aOR 0.54, p <0.001). Additionally, the lowest evaluation and treatment rates were among Asian patients from the West, but no difference was observed between non-Asian patients and Asian patients from the East. Asian patients from the West (vs. East) were about 40-50% less likely to undergo adequate evaluation (aOR 0.60) and initiate NAs (aOR 0.54) (both p <0.001). CONCLUSIONS: Evaluation and treatment rates were suboptimal for patients with CHB in both the East and West, with significant sex and ethnic disparities. Improved linkage to care with linguistically competent and culturally sensitive approaches is needed. IMPACT AND IMPLICATIONS: Significant sex and ethnic disparities exist in hepatitis B evaluation and treatment, with female treatment-eligible patients about 50% less likely to receive antiviral treatment and Asian patients from Western regions also about 50% less likely to receive adequate evaluation or treatment compared to Asians from the East (there was no significant difference between Asian patients from the East and non-Asian patients). Improved linkage to care with linguistically competent and culturally sensitive approaches is needed.

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BACKGROUND: Melanocytic nevi typically appear in early childhood, and their removal is a common plastic surgery procedure performed on children. However, the epidemiological characteristics and hospitalization burden of children with melanocytic nevi have rarely been described in detail. METHODS: Medical records of pediatric inpatients with melanocytic nevi from January 1, 2016, to December 31, 2021, were collected from the Futang Research Center of Pediatric Development database in China. We then extracted and statistically analyzed the relevant information, including demographic characteristics, clinical information, hospitalization burden, and other basic information for each inpatient. RESULTS: Among the 13,396 inpatients with melanocytic nevi, the highest number of cases was found in East China, and most patients were residents of urban areas. Most hospitalized patients consisted of boys aged 7-12 years with melanocytic nevi. Lesion sites in the buttocks and lower limbs were most common among pediatric inpatients with melanocytic nevi. Compound nevi were the most common (38.50 %) histological subtype and the rate of conversion into melanoma was 1.02 % (137 inpatients) among pediatric inpatients with melanocytic nevi. The hospitalization burden for patients varied significantly based on factors such as the age of the patients undergoing surgery, year of hospitalization, site of the lesion, histological subtype, and surgical method. In general, if the patients' age was under 1 year, lesion site was located in face, and there was a need for excision combined with tissue expander can significantly increase the treatment fees for pediatric inpatients with melanocytic nevi. CONCLUSION: Given the increasing number and relatively large hospitalization burden among children with melanocytic nevi hospitalized in China, the government needs to pay more attention to this group and provide corresponding economic and policy support.

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In a multi-center randomized controlled trial (RCT) with competitive recruitment, eligible patients are enrolled sequentially by different study centers and are randomized to treatment groups using the chosen randomization method. Given the stochastic nature of the recruitment process, some centers may enroll more patients than others, and in some instances, a center may enroll multiple patients in a row, for example, on a given day. If the study is open-label, the investigators might be able to make intelligent guesses on upcoming treatment assignments in the randomization sequence, even if the trial is centrally randomized and not stratified by center. In this paper, we use enrollment data inspired by a real multi-center RCT to quantify the susceptibility of two restricted randomization procedures, the permuted block design and the big stick design, to selection bias under the convergence strategy of Blackwell and Hodges (1957) applied at the center level. We provide simulation evidence that the expected proportion of correct guesses may be greater than 50% (i.e., an increased risk of selection bias) and depends on the chosen randomization method and the number of study patients recruited by a given center that takes consecutive positions on the central allocation schedule. We propose some strategies for ensuring stronger encryption of the randomization sequence to mitigate the risk of selection bias.

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