Correlation of Residents’ Performance in Competency-Based Exams and Orthopaedic In- Training Examinations (OITE)
Bibliographic record
Abstract
Abstract Background: The research team aimed to assess the relationship between performance in a competency-based curriculum (CBC) evaluation, and the Orthopaedic In-Training Examination (OITE) in the Postgraduate Year 1 (PGY1) cohort of 2016-2017.Methods: After development of the ‘Basic Trauma’ (BHT) and ‘Basic Arthroplasty’ (BA) CBC modules, assessment consisted of multiple-choice questions (MCQ), objective structured clinical evaluation (OSCE), structured oral panels, and the OITE were conducted annually. We collated MCQ and OSCE evaluations for BHT and BA, as well as the OITE result for the same cohort from PGY1 and the end of PGY2. We evaluated the OITE score difference for correlation with the scores attained for the two CBC modules.Results: Among all participants (n=9), there was a significant improvement in mean OITE scores from PGY1 to PGY2 (43.78% (±4.09) to 56.67% (±4.24); t-test p= 0.00). There was no significant correlation between OITE improvement, and scores attained in the BHT and BA modules, nor between BHT and BA results and the ‘Hip and Knee’ and ‘Trauma’ domains of the OITE exam.Conclusions: Improvement in OITE performance is not dependent on evaluated CBC modules. Further research to determine what factors play a role in trainee improvement in objective performance is required.
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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 | 0.015 |
| 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 | 0.000 | 0.000 |
| Open science | 0.000 | 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".