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Unlabelled: We previously developed and validated LAPDOCTOR (LAParoscopic-Donor-nephreCTomy-scORe), a novel scoring system for the preoperative assessment of the difficulty of living donor nephrectomy (LDN). To prove its significance, we extended our investigation to a prospective, multicenter, national study. Difficulty was assessed by the operating surgeon using a scale from 1 to 3 (1-standard, 2-moderately difficult, 3-very difficult) based on eight parameters: availability of laparoscopic space, mobilization of the colon, kidney, gonadal, adrenal and renal vein, renal artery, and ureter. Donor CT-scans were blindly reviewed by a radiologist, and the LAPDOCTOR scores were compared with the difficulty levels assigned by the surgeon to investigate the match rates. One hundred eighty-five donors were enrolled, with a mean age of 54 years (range 24-77), BMI 25 kg/m2 (range 17-35), and male/female 59/126. LDN was blindly scored as standard in 45% of the cases, moderately-difficult in 52%, and very-difficult in 3%. The agreement between the LAPDOCTOR and expert donor surgeons' rate in categorizing LDN into risk groups had a QWK of 0.711 (95% CI 0.577-0.844) with p < 0.001. The LAPDOCTOR enables precise preoperative determination of the difficulty of LDN, particularly in very difficult cases, and assessment of surgical risk in living kidney donors.
Clinical Trial Notation: https://ClinicalTrials.gov, Identifier NCT05769686.
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http://dx.doi.org/10.3389/ti.2025.14100 | DOI Listing |
JMIR Med Inform
September 2025
Departments of Radiology, The Third Affiliated Hospital, Sun Yat-Sen University, 600 Tianhe Road, Guangzhou, Guangdong, 510630, China, 86 18922109279, 86 20852523108.
Background: Despite the Coronary Artery Reporting and Data System (CAD-RADS) providing a standardized approach, radiologists continue to favor free-text reports. This preference creates significant challenges for data extraction and analysis in longitudinal studies, potentially limiting large-scale research and quality assessment initiatives.
Objective: To evaluate the ability of the generative pre-trained transformer (GPT)-4o model to convert real-world coronary computed tomography angiography (CCTA) free-text reports into structured data and automatically identify CAD-RADS categories and P categories.
JMIR Res Protoc
September 2025
Department of Urology, Faculty of Medicine, Universitas Indonesia - Cipto Mangunkusumo Hospital, Jakarta, Indonesia.
Background: Circumcision is a widely practiced procedure with cultural and medical significance. However, certain penile abnormalities-such as hypospadias or webbed penis-may contraindicate the procedure and require specialized care. In low-resource settings, limited access to pediatric urologists often leads to missed or delayed diagnoses.
View Article and Find Full Text PDFJ Craniofac Surg
September 2025
Department of Oral and Maxillofacial Surgery, University of Ulsan Hospital, University of Ulsan College of Medicine.
This study aimed to develop a deep-learning model for the automatic classification of mandibular fractures using panoramic radiographs. A pretrained convolutional neural network (CNN) was used to classify fractures based on a novel, clinically relevant classification system. The dataset comprised 800 panoramic radiographs obtained from patients with facial trauma.
View Article and Find Full Text PDFJMIR Public Health Surveill
September 2025
Hospital Israelita Albert Einstein, 755 Comendador Elias Jafet Street, L1 Floor, Room 134, São Paulo, 05653-000, Brazil.
Background: The Brazilian project, launched in 2021, aims to establish a nationwide injury registry that systematically collects detailed information on incidents and individuals across the country, regardless of injury severity. The registry integrates information from prehospital and hospital care, various health systems lacking interoperability, and data from sectors such as firefighters and police. Its primary aim is to enhance health surveillance by providing timely, high-quality information that guides prevention strategies and informs policymaking.
View Article and Find Full Text PDFJ Cataract Refract Surg
September 2025
Ophthalmology Unit, Department of Medicine, Surgery and Neurosciences, University of Siena, Siena, Italy.
Purpose: To compare the usability and training effectiveness of a 3D-printed coaxial illumination system mounted on an off-the-shelf stereo-microscope to a professional ophthalmic surgical microscope, in cataract surgery simulation.
Setting: Ophthalmology Lab, Ophthalmology Unit, Department of Medicine, Surgery and Neurosciences, University of Siena, Siena, Italy.
Design: Prospective randomized crossover study.