Increasing Research Productivity and Step 2 Score Among Matched Orthopaedic Surgery Residents: A Forecasting Analysis to 2040
Bibliographic record
Abstract
BACKGROUND: Orthopaedics has consistently been among the most competitive residency specialties in which to match. The purpose of this study was to evaluate trends in important data as provided by the National Resident Matching Program regarding orthopaedic surgery residency and to project future averages for successful applicants. METHODS: "Charting Outcomes in the Match" are biennial reports published by the National Resident Matching Program, listing applicant characteristics stratified by specialty. We collected data between 2014 and 2024 for allopathic medical school seniors from the United States that successfully matched into orthopaedic surgery residency. Regression analysis was performed to predict research productivity and United States Medical Licensing Examination Step 2 scores to the year 2040. RESULTS: Research productivity and Step 2 score have shown notable trends among matched orthopaedic surgery residency applicants, with research productivity following an exponential increase ( R2 = 0.988, P < 0.001) and Step 2 scores following a linear trend ( R2 = 0.925, P = 0.002). In accordance with these models, by the year 2040, matched applicants are projected to have an average of 165.6 (95% prediction interval, 107.8 to 254.5) publications, posters, abstracts, and presentations and an average Step 2 score of 266 (95% prediction interval, 261 to 271) points, outperforming the 87th percentile. Specifically, Step 2 score is projected to increase by 1.1 point every 2 years with the limitation that the upper limit score is 300. CONCLUSION: Research productivity and USMLE Step 2 scores among matched orthopaedic surgery residency applicants are projected to increase in the coming years. These findings can inform medical school administration, residency programs, and orthopaedic leadership to optimize their programs for student, resident, and organizational success. Moreover, it may be time to reconsider the importance of these metrics as a perpetual increase in scores and research activity is not sustainable nor equitable.
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How this classification was reachedexpand
Full frame machine prediction
Teacher imitationNot calibrated prevalence, not ground truth. Human validation pending. The Gemma side is a direct model label for every work in the frame, read from the title-only record. The Codex side is a classifier learned from the 10,348 direct Codex labels and calibrated to design-weighted sample rates; fields without enough sample support carry no Codex call. Candidate is the union of the two sides; consensus is their intersection. These outputs are machine_predicted_unvalidated and are not human labels.
Distilled classifier scores by category (both heads)
| Category | Codex | Gemma |
|---|---|---|
| Metaresearch | 0.003 | 0.007 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.001 |
| Bibliometrics | 0.002 | 0.002 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.001 | 0.001 |
| Open science | 0.001 | 0.001 |
| Research integrity | 0.001 | 0.001 |
| Insufficient payload (model declined to judge) | 0.002 | 0.001 |
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 source (direct Gemma or distilled Codex), 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".