The Educational Impact of a Fellowship-trained Orthopaedic Oncologist
Bibliographic record
Abstract
INTRODUCTION: Musculoskeletal oncology is a subspecialty of orthopaedics with few fellowship-training locations. Although orthopaedic oncologists comprise a minority within the field of orthopaedic surgery, most work at academic centers and serve in leadership roles with notable impact on patients and the training of residents. This article investigates the objective impact orthopaedic oncologists have regarding resident operative case volume and performance on in-training examinations. METHODS: The William Beaumont Army Medical Center and Texas Tech University Health Sciences Center of El Paso combined orthopaedic residency program's case logs and Orthopaedic In-Training Examination (OITE) scores between 2013 and 2018 were reviewed. This provided 3 academic years of data before and after an orthopaedic oncology faculty member arrived in 2016. The case volume and OITE examination performance before and after the addition of the orthopaedic oncology faculty member were compared. RESULTS: After the addition of an orthopaedic oncology faculty member, a significant increase was observed in the program's OITE overall correctly answered questions (171.30 versus 181.03, P = 0.004) and oncology subsection percentile (56th to the 66th percentile, P = 0.038). An increase was also observed in resident oncology case volume from 29 oncology cases per year to 138 cases on average (P = 0.022). DISCUSSION: The addition of a fellowship-trained orthopaedic oncologist results in increased exposure to orthopaedic oncology cases and improved resident performance on the OITE. This may correlate to improved American Board of Orthopaedic Surgeons Part I pass rates and improved overall resident satisfaction.
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How this classification was reachedexpand
Full frame distilled prediction
Teacher imitationNot calibrated prevalence, not ground truth. Human validation pending. Learned from the 10,348 direct Codex labels and 10,348 direct Gemma labels. Candidate is the union of thresholded teacher heads; consensus is their intersection. These outputs are machine_predicted_unvalidated and are not human labels or direct frontier model labels.
Codex and Gemma teacher scores by category
| Category | Codex | Gemma |
|---|---|---|
| Metaresearch | 0.001 | 0.004 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.000 | 0.001 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.000 | 0.000 |
| Open science | 0.000 | 0.000 |
| Research integrity | 0.000 | 0.000 |
| Insufficient payload (model declined to judge) | 0.000 | 0.000 |
Machine scores (provisional)
The two teacher heads of the student model, read on this work. A score orders the frame for review; it never asserts a category, and the validation status ships verbatim with every row.
Baseline scores from an immature model (maturity gate not passed, 7 training rounds). Scores rank; they never assert a category.
score_only:v0-immature-baseline · verbatim from the scoring run: score_only means the number may rank works, and no category label ships from itClassification
machine, unvalidatedMachine predicted; a candidate call from one teacher head, not a consensus.
How this classification was reached, model by model and score by score, is at the end of the page under "How this classification was reached".