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Objective: To systematically evaluate the performance and applicability of risk prediction models for complications after flap repair and to provide guidance for building and refining models.
Methods: PubMed, Embase, Web of Science, the Cochrane Library, CNKI, SinoMed, VIP and Wanfang were searched for studies on risk prediction models for flap complications. The search period is from inception to December 28, 2024. The PROBAST tool was used to evaluate the quality of the prediction model research, and Stata 18 software was employed to meta-analyze the predictors of the models.
Results: A total of 16 studies were included, 28 risk prediction models were constructed, and the area under the receiver operating characteristic curve (AUC) ranged from 0.655 to 0.964, with 16 prediction models performing well (AUC > 0.7). Eleven articles underwent model calibration, 16 were validated internally, and 3 were validated externally. The results of the PROBAST review revealed that all 16 studies were at high risk of bias. The incidence rate of flap complications was 14.8% (95% CI, 10.7 - 19.0%). Body mass index (BMI), smoking history, long flap reconstruction time, diabetes mellitus, hypertension, and postoperative infection were independent risk factors for complications after flap repair (P < 0.05).
Conclusion: The risk prediction model for complications after flap repair has certain predictive value, but the overall risk of bias is high, and there is a lack of external validation; thus, it needs to be further enhanced and optimized to increase its prediction accuracy and clinical practicability.
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http://dx.doi.org/10.1186/s12893-025-03072-8 | DOI Listing |
Crit Rev Ther Drug Carrier Syst
January 2025
Department of Pharmacology, PSG College of Pharmacy, Coimbatore 641004, Tamil Nadu, India.
Treating neurological disorders is challenging due to the blood-brain barrier (BBB), which limits therapeutic agents, including proteins and peptides, from entering the central nervous system. Despite their potential, the BBB's selective permeability is a significant obstacle. This review explores recent advancements in protein therapeutics for BBB-targeted delivery and highlights computational tools.
View Article and Find Full Text PDFNeural Netw
September 2025
School of Automation and Intelligent Sensing, Shanghai Jiao Tong University, Shanghai, 200240, China; Institute of Image Processing and Pattern Recognition, Shanghai Jiao Tong University, Shanghai, 200240, China; Institute of Medical Robotics, Shanghai Jiao Tong University, Shanghai, 200240, China.
3D shape defect detection plays an important role in autonomous industrial inspection. However, accurate detection of anomalies remains challenging due to the complexity of multimodal sensor data, especially when both color and structural information are required. In this work, we propose a lightweight inter-modality feature prediction framework that effectively utilizes multimodal fused features from the inputs of RGB, depth and point clouds for efficient 3D shape defect detection.
View Article and Find Full Text PDFAtherosclerosis
September 2025
Department of Cardiothoracic and Macrovascular Surgery, Jingzhou Hospital Affiliated to Yangtze University, No.26 Chuyuan Avenue, Jingzhou District, Jingzhou City, Hubei Province, 434020, China. Electronic address:
Background And Aims: Aortic dissection (AD) is one of the most dangerous and tricky diseases in the field of cardiovascular surgery, severely affecting public health. Recent studies have found that SUMOylation is linked to the pathogenesis of cardiovascular diseases. However, we know very little about the molecular mechanisms of SUMOylation in AD.
View Article and Find Full Text PDFJ Med Internet Res
September 2025
Department of Information Systems and Cybersecurity, The University of Texas at San Antonio, 1 UTSA Circle, San Antonio, TX, 78249, United States, 1 (210) 458-6300.
Background: Adverse drug reactions (ADR) present significant challenges in health care, where early prevention is vital for effective treatment and patient safety. Traditional supervised learning methods struggle to address heterogeneous health care data due to their unstructured nature, regulatory constraints, and restricted access to sensitive personal identifiable information.
Objective: This review aims to explore the potential of federated learning (FL) combined with natural language processing and large language models (LLMs) to enhance ADR prediction.
Ann Intern Med
September 2025
Department of Medicine, Johns Hopkins University School of Medicine, and Department of Epidemiology, Johns Hopkins Bloomberg School of Public Health, Baltimore, Maryland (J.B.S.).
Electronic health record (EHR) data are increasingly used to develop prediction models that guide clinical decision making at the point of care. These include algorithms that use high-frequency data, like in sepsis prediction, as well as simpler equations, such as the Pooled Cohort Equations for cardiovascular outcome prediction. Although EHR data used in prediction models are often highly granular and more current than other data, there is systematic and nonsystematic missingness in EHR data as there is with most data.
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