The historic predictive value of Canadian orthopedic surgery residents’ orthopedic in-training examination scores on their success on the RCPSC certification examination
Bibliographic record
Abstract
BACKGROUND: Positive correlation between the orthopedic in-training examination (OITE) and success in the American Board of Orthopaedic Surgery examination has been reported. Canadian training programs in internal medicine, anesthesiology and urology have found a positive correlation between in-training examination scores and performance on the Royal College of Physicians and Surgeons of Canada (RCPSC) certification examination. We sought to determine the potential predictive value of the OITE scores of Canadian orthopedic surgery residents on their success on their RCPSC examinations. METHODS: A total of 118 Canadian orthopedic surgery residents had their annual OITE scores during their 5 years of training matched to the RCPSC examination oral and multiple-choice questions and to overall examination pass/fail scores. We calculated Pearson correlations between the in-training examination for each postgraduate year and the certification oral and multiple-choice questions and pass/fail marks. RESULTS: There was a predictive association between the OITE and success on the RCPSC examination. The association was strongest between the OITE and the written multiple-choice examination and weakest between the OITE and the overall examination pass/fail marks. CONCLUSION: Overall, the OITE was able to provide useful feedback to Canadian orthopedic surgery residents and their training programs in preparing them for their RCPSC examinations. However, when these data were collected, truly normative data based on a Canadian sample were not available. Further study is warranted based on a more refined analysis of the OITE, which is now being produced and includes normative percentile data based on Canadian residents.
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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 | 0.002 | 0.016 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.002 | 0.002 |
| Science and technology studies | 0.001 | 0.001 |
| Scholarly communication | 0.001 | 0.000 |
| Open science | 0.001 | 0.001 |
| Research integrity | 0.000 | 0.001 |
| Insufficient payload (model declined to judge) | 0.002 | 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 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".