Introduction: Increasingly, national accrediting bodies and professional societies for musculoskeletal oncology recognize the need for more standardized training. This study elucidates recent trends in reported case volume during Accreditation Council for Graduate Medical Education (ACGME)-accredited musculoskeletal oncology fellowship training relative to case minimum requirements.

Methods: We conducted a retrospective cross-sectional analysis of fellows at ACGME-accredited musculoskeletal oncology fellowships (2017 to 2022). Percentiles in reported case volumes were calculated across ACGME-defined case categories and temporal changes assessed by linear regression. Variability between the highest (90th percentile) and lowest (10th percentile) deciles was calculated as fold differences. Sensitivity analyses were conducted to estimate the number of fellows not meeting ACGME-defined case minimum requirements.

Results: Case logs from 95 musculoskeletal oncology fellows were analyzed. From 2017 to 2022, total relevant oncology procedures increased from 191 ± 49 to 228 ± 73 ( P = 0.066). Pediatric oncology accounted for a minority of cases (range, 6 to 8%). A mean of 222 total relevant oncology procedures were reported. Most were in management of metastatic disease (21%), soft-tissue resection/reconstruction (20%), and limb salvage (13%). Variability in total relevant oncology procedures was 2.6 and greatest in spine/pelvis (4.6), pediatric oncologic cases (4.4), and surgical management of complications (4.4). No clear trends were observed in case volume variability over the study period ( P > 0.05). Analysis of case volume percentiles identified at least 30% of musculoskeletal oncology fellows not achieving minimum requirements for pediatric oncologic cases (n = 29 fellows) and 10% of fellows not achieving minimum requirements for total relevant oncology procedures (n = 10 fellows).

Discussion: Results from this study may help future musculoskeletal oncology fellows and faculty identify potential areas to increase case exposure and reduce variability during fellowship training. More investigation is needed to determine evidence-based case minimum requirements including surgical learning curves and other competency-based assessment tools in musculoskeletal oncology.

Download full-text PDF

Source
http://dx.doi.org/10.5435/JAAOS-D-24-00012DOI Listing

Publication Analysis

Top Keywords

musculoskeletal oncology
32
case volume
16
oncology fellows
16
total relevant
16
relevant oncology
16
oncology procedures
16
oncology
13
2017 2022
12
case minimum
12
minimum requirements
12

Similar Publications

Heightened clinical vigilance for multiple myeloma is essential in patients presenting with atypical chronic pain progression. Symptoms may overlap with degenerative musculoskeletal conditions, frequently leading to misdiagnosis. This underscores the necessity of a thorough evaluation when symptoms are refractory to conventional therapies, in order to facilitate timely diagnosis and effective management of malignancy.

View Article and Find Full Text PDF

Purpose: Abdominal wall endometriosis consists of endometrial tissue between the peritoneum and the abdominal wall. The established treatment involves amenorrheic drugs-not always successful and tolerated-or invasive surgery. In this scenario, minimally invasive techniques such as cryoablation are a potential option.

View Article and Find Full Text PDF

Introduction: Numerous challenges hinder the development of multidisciplinary medical education in a resource-constrained environment. Communal tumour boards built through networking could be a suitable model for the effective management of diseases and enhancement of medical education. This study evaluated the impact of an integrated care pathway for patients with musculoskeletal tumours via multi-institutional networking in a metropolis.

View Article and Find Full Text PDF

Purpose: To provide a fast quantitative imaging approach for a 0.55T scanner, where signal-to-noise ratio is limited by the field strength and k-space sampling speed is limited by a lower specification gradient system.

Methods: We adapted the three-dimensional spiral projection imaging MR fingerprinting approach to 0.

View Article and Find Full Text PDF

Larger sample sizes are needed when developing a clinical prediction model using machine learning in oncology: methodological systematic review.

J Clin Epidemiol

January 2025

Centre for Statistics in Medicine, Nuffield Department of Orthopaedics, Rheumatology and Musculoskeletal Sciences, University of Oxford, Oxford, OX3 7LD, UK.

Background: Having a sufficient sample size is crucial when developing a clinical prediction model. We reviewed details of sample size in studies developing prediction models for binary outcomes using machine learning (ML) methods within oncology and compared the sample size used to develop the models with the minimum required sample size needed when developing a regression-based model (N).

Methods: We searched the Medline (via OVID) database for studies developing a prediction model using ML methods published in December 2022.

View Article and Find Full Text PDF

Want AI Summaries of new PubMed Abstracts delivered to your In-box?

Enter search terms and have AI summaries delivered each week - change queries or unsubscribe any time!