Background: Measures of quality in resident training in plastic and reconstructive surgery (PRS) programs are scarce and often methodologically inconsistent. Our research provides insights from current PRS trainees globally, mapping their training inputs, expected outputs, and recommendations for program improvements.
Methods: A global online survey was conducted among PRS residents across 70 countries to gauge their satisfaction with residency training, capturing training inputs such as the number of surgeries attended and seminars they participated in. We also extracted residents' proposed recommendations for program improvement. We investigated the explanatory role of training inputs, demographics, hospital characteristics, and country income on resident satisfaction and graduate competence.
Results: The analysis incorporated data from 518 PRS residents. On average, residents attended 9.8 surgeries and 1.3 seminars per week. Simultaneously, there was a positive correlation between the perceived level of professional competency and training inputs, particularly seminars attended (p - value = 0.001). Male residents tended to report higher satisfaction (p - value = 0.045) with their training (67%) compared with their female counterparts (58%), while those with family responsibilities also demonstrated slightly higher satisfaction levels.
Conclusions: Our analysis expands the evidence base regarding a "global hunger" for more comprehensive seminar-based and hands-on surgical training. Resident recommendations on program improvement reveal the need to address gaps, particularly in aesthetic surgery training. The development of healthcare business models that allow for aesthetic procedures in training institutions is crucial in the promotion of aesthetic surgery training during residency. Policymakers, program directors, and stakeholders across the world can leverage these findings to formulate policies addressing the weaknesses of training programs.
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http://dx.doi.org/10.1007/s00266-024-04588-9 | DOI Listing |
Biomed Eng Online
December 2024
Department of Clinical Physiology, Motion Analysis Center, University Hospital of Toulouse, Hôpital de Purpan, Toulouse, France.
Background: Stroke is the leading cause of acquired motor deficiencies in adults. Restoring prehension abilities is challenging for individuals who have not recovered active hand opening capacities after their rehabilitation. Self-triggered functional electrical stimulation applied to finger extensor muscles to restore grasping abilities in daily life is called grasp neuroprosthesis (GNP) and remains poorly accessible to the post-stroke population.
View Article and Find Full Text PDFMethods
December 2024
Center for Research Innovation and Biomedical Informatics, Faculty of Medical Technology, Mahidol University, Bangkok 10700, Thailand. Electronic address:
Identifying angiotensin-I-converting enzyme (ACE) inhibitory peptides accurately is crucial for understanding the primary factor that regulates the renin-angiotensin system and for providing guidance in developing new potential drugs. Given the inherent experimental complexities, using computational methods for in silico peptide identification could be indispensable for facilitating the high-throughput characterization of ACE inhibitory peptides. In this paper, we propose a novel deep stacking-based ensemble learning framework, termed Deepstack-ACE, to precisely identify ACE inhibitory peptides.
View Article and Find Full Text PDFInt J Med Inform
December 2024
Department of Medical Informatics, Amsterdam Public Health Research Institute, Amsterdam UMC, University of Amsterdam, the Netherlands; Institute of Logic, Language and Computation, University of Amsterdam, the Netherlands; Pacmed, Amsterdam, the Netherlands. Electronic address:
Background: Machine Learning (ML) models often struggle to generalize effectively to data that deviates from the training distribution. This raises significant concerns about the reliability of real-world healthcare systems encountering such inputs known as out-of-distribution (OOD) data. These concerns can be addressed by real-time detection of OOD inputs.
View Article and Find Full Text PDFJ Fr Ophtalmol
December 2024
Department of Ophthalmology and Vision Sciences, University of Toronto, Toronto, Ontario, Canada; Department of Ophthalmology, St. Michael's Hospital/Unity Health Toronto, Toronto, Ontario, Canada. Electronic address:
Purpose: Prior literature has suggested a reduced performance of large language models (LLMs) in non-English analyses, including Arabic and French. However, there are no current studies testing the multimodal performance of ChatGPT in French ophthalmology cases, and comparing this to the results observed in the English literature. We compared the performance of ChatGPT-4 in French and English on open-ended prompts using multimodal input data from retinal cases.
View Article and Find Full Text PDFComput Biol Med
December 2024
Inria, Univ. Grenoble Alpes, CNRS, Grenoble INP, LJK, France.
This study introduces a novel deep learning approach for 3D teeth scan segmentation and labeling, designed to enhance accuracy in computer-aided design (CAD) systems. Our method is organized into three key stages: coarse localization, fine teeth segmentation, and labeling. In the teeth localization stage, we employ a Mask-RCNN model to detect teeth in a rendered three-channel 2D representation of the input scan.
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