Publications by authors named "M Carmen Menendez"

Introduction: Shoulder and elbow surgery fellowships in the United States are recognized for their academic emphasis, yet the correlation between an applicant's research productivity and fellowship match results remains unclear. This study (1) analyzed temporal trends in research productivity among matched fellowship applicants, (2) evaluated the influence of quantity of publications and first authorships on match positions, and (3) investigated program variations in research productivity.

Methods: This is a retrospective analysis of data from the San Francisco (SF) Match Database for shoulder and elbow surgery applicants matched between 2017 and 2024.

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Background: Despite the effectiveness of remplissage in reducing instability recurrence, debate remains about the loss of external rotation (ER) after this procedure.

Purpose: To compare the loss of ER after primary isolated arthroscopic Bankart repair alone (BR), Bankart with remplissage (REMP), and Latarjet (LAT) procedures.

Study Design: Meta-analysis; Level of evidence, 3.

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Hypothesis And Background: Reverse shoulder arthroplasty (RSA) exhibits favorable outcomes in managing rotator cuff arthropathy, primary glenohumeral arthritis, and complex proximal humeral fractures. Despite its success and reliability, certain patients experience persistent pain and stiffness. The clinical utility of therapeutic arthroscopy in RSA patients remains an area for investigation.

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International medical graduates (IMGs) are integral to the US healthcare system but are scarce in orthopaedic residency classes. Positive contributions of IMGs to the field of orthopaedic surgery in the United States are well documented, but successfully matching into an orthopaedic residency position as an IMG remains very challenging. The purpose of this study was to review current processes, strategies, and potential barriers of IMGs applying for orthopaedic surgery residency in the United States as an IMG.

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Article Synopsis
  • This study focuses on identifying extraprostatic extension (EPE) in prostate cancer patients using advanced imaging techniques, highlighting its importance for treatment outcomes.
  • Researchers analyzed MRI scans from 20 patients who underwent surgery, extracting over 200 radiomic features to help differentiate between EPE and non-EPE lesions.
  • The results showed that an MLP neural network model using 18 key features could accurately predict EPE, achieving a sensitivity of 75%, specificity of 79%, and an area-under-curve (AUC) of 0.82, indicating promising predictive capability.
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