Factors Predictive of Orthopaedic In-training Examination Performance and Research Productivity Among Orthopaedic Residents
Bibliographic record
Abstract
INTRODUCTION: Selection of qualified candidates for orthopaedic residency is necessary for growth and innovation. The purpose of this study was to determine predictors of Orthopaedic In-training Exam (OITE) performance and research productivity. METHODS: A survey was distributed to 13 residency programs collecting demographics, United States Medical Licensing Examination (USMLE) and OITE scores, and authored publications. Associations between preresidency qualifications and OITE scores and publications were determined. RESULTS: A total of 274 of 294 surveys were returned (93.2%). We found a positive correlation between USMLE step 1 and 2 scores with recent OITE percentile (P < 0.001). Preresidency authorship (P < 0.001) and postgraduate training year (P < 0.001) were independent predictors of authorship during residency, whereas USMLE step 1 score was not (P = 0.094). CONCLUSION: Candidates who perform well on the USMLE are likely to perform well on the OITE, whereas those with greater authored publications are likely to continue research during residency.
Fetched live from OpenAlex and de-inverted. Abstracts are not stored in this database: the inverted indexes are 8.6 GB of the frame’s 9.3 GB of text, and the host has 13 GB free.
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 | 0.001 | 0.010 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.001 | 0.001 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.001 | 0.000 |
| Open science | 0.000 | 0.001 |
| Research integrity | 0.000 | 0.000 |
| Insufficient payload (model declined to judge) | 0.005 | 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".