Setting pass scores for assessment of technical performance by surgical trainees
Bibliographic record
Abstract
BACKGROUND: One of the major challenges of competency-based training is defining a score representing a competent performance. The objective of this study was to set pass scores for the Objective Structured Assessment of Technical Skill. METHODS: Pass scores for the examination were set using three standard setting methods applied to data collected prospectively from first-year surgical residents (trainees). General surgery residents were then assigned an overall pass-fail status for each method. Using a compensatory model, residents passed the eight station examinations if they met the overall pass score; using a conjunctive model, residents passed if they met the overall pass score and passed at least 50 per cent of the stations. The consistency of the pass-fail decision across the three methods, and between a compensatory and conjunctive model, were compared. RESULTS: Pass scores were stable across all three methods using data from 513 residents, 133 of whom were general surgeons. Consistency of the pass-fail decision across the three methods was 95.5 and 93.2 per cent using compensatory and conjunctive models respectively. Consistency of the pass-fail status between compensatory and conjunctive models for all three methods was also very high (91.7, 95.5 and 96.2 per cent). CONCLUSION: Consistency in pass-fail status between the various methods builds evidence of validity for the set scores. These methods can be applied and studied across a variety of assessment platforms, helping to increase the use of standard setting for competency-based training.
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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 | 0.017 | 0.051 |
| Meta-epidemiology (narrow) | 0.001 | 0.000 |
| Meta-epidemiology (broad) | 0.001 | 0.001 |
| Bibliometrics | 0.005 | 0.002 |
| Science and technology studies | 0.001 | 0.001 |
| Scholarly communication | 0.001 | 0.001 |
| Open science | 0.002 | 0.002 |
| Research integrity | 0.001 | 0.001 |
| Insufficient payload (model declined to judge) | 0.005 | 0.002 |
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".