Background: Anterior shoulder instability, including recurrent instability, is a common problem, particularly in young, active patients and contact athletes. The Latarjet procedure is a common procedure to treat recurrent shoulder instability.
Purpose: To identify the reported learning curves associated with the Latarjet procedure and to determine a point on the learning curve after which a surgeon can be considered to have achieved proficiency.
Study Design: Systematic review; Level of evidence, 4.
Methods: Three online databases (Embase, MEDLINE, PubMed) were systematically searched and screened in duplicate by 2 independent reviewers. The search included results from the inception of each database to January 23, 2017. Data regarding study characteristics, patient demographics, learning curve analyses, and complications were collected. Study quality was assessed in duplicate.
Results: Two level 3 studies and 3 level 4 studies of fair methodological quality were included. Overall, 349 patients (350 shoulders) with a mean age of 25.1 years (range, 14-52 years) were included in the final data analysis. Patients were predominantly male (93.7%). After 22 open and 20 to 40 arthroscopic Latarjet procedures, surgeons achieved a level of proficiency as measured by decreased operative time. For open procedures, complication rates and lengths of hospital stay decreased significantly with increased experience (Spearman ρ = -0.3, = .009 and Spearman ρ = -0.6, < .0001, respectively).
Conclusion: With experience, surgeons achieved a level of proficiency in performing arthroscopic and open Latarjet procedures, as measured by decreased operative time, length of hospital stay, and complication rate. The most commonly reported difference was operative time, which was significant across all studies. Overall, the Latarjet procedure is a safe procedure with low complication rates, although further research is required to truly characterize this learning curve.
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http://www.ncbi.nlm.nih.gov/pmc/articles/PMC6077900 | PMC |
http://dx.doi.org/10.1177/2325967118786930 | DOI Listing |
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Although radiotherapy techniques are the primary treatment for head and neck cancer (HNC), they are still associated with substantial toxicity, and side effect. Machine learning (ML) based radiomics models for predicting toxicity mostly rely on features extracted from pre-treatment imaging data. This study aims to compare different models in predicting radiation-induced xerostomia and sticky saliva in both early and late stage of HNC patients using CT and MRI image features along with demographics and dosimetric information.
View Article and Find Full Text PDFPLoS One
January 2025
Department of Computer Science, Faculty of Computing, Federal University of Lafia, Lafia, Nasarawa State, Nigeria.
Med Phys
January 2025
Deparment of Radiation Oncology, Duke University, Durham, North Carolina, USA.
Background: Stereotactic radiosurgery (SRS) is widely used for managing brain metastases (BMs), but an adverse effect, radionecrosis, complicates post-SRS management. Differentiating radionecrosis from tumor recurrence non-invasively remains a major clinical challenge, as conventional imaging techniques often necessitate surgical biopsy for accurate diagnosis. Machine learning and deep learning models have shown potential in distinguishing radionecrosis from tumor recurrence.
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January 2025
Institute for Artificial Intelligence in Medicine, University Hospital Essen, Germany.
Background: This study aimed to develop an automated algorithm to noninvasively distinguish gliomas from other intracranial pathologies, preventing misdiagnosis and ensuring accurate analysis before further glioma assessment.
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