Passing a Technical Skills Examination in the First Year of Surgical Residency Can Predict Future Performance
Bibliographic record
Abstract
ABSTRACT Background The ability of an assessment to predict performance would be of major benefit to residency programs, allowing for early identification of residents at risk. Objective We sought to establish whether passing the Objective Structured Assessment of Technical Skills (OSATS) examination in postgraduate year 1 (PGY-1) predicts future performance. Methods Between 2002 and 2012, 133 PGY-1 surgery residents at the University of Toronto (Toronto, Ontario, Canada) completed an 8-station, simulated OSATS examination as a component of training. With recently set passing scores, residents were assigned a pass/fail status using 3 standards setting methods (contrasting groups, borderline group, and borderline regression). Future in-training performance was compared between residents who had passed and those who failed the OSATS, using in-training evaluation reports from resident files. A Mann-Whitney U test compared performance among groups at PGY-2 and PGY-4 levels. Results Residents who passed the OSATS examination outperformed those who failed, when compared during PGY-2 across all 3 standard setting methodologies (P < .05). During PGY-4, only the contrasting groups method showed a significant difference (P < .05). Conclusions We found that PGY-1 surgical resident pass/fail status on a technical skills examination was associated with future performance on in-training evaluation reports in later years. This provides validity evidence for the current PGY-1 pass/fail score, and suggests that this technical skills examination may be used to predict performance and to identify residents who require remediation.
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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 | 0.001 | 0.007 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.001 | 0.000 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.000 | 0.000 |
| Open science | 0.000 | 0.001 |
| Research integrity | 0.000 | 0.000 |
| 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".