Systematic review to establish absolute standards for technical performance in surgery
Bibliographic record
Abstract
BACKGROUND: Standard setting allows educators to create benchmarks that distinguish between those who pass and those who fail an assessment. It can also be used to create standards in clinical and simulated procedural skill. The objective of this review was to perform a systematic review of the literature using absolute standard-setting methodology to create benchmarks in technical performance. METHODS: A systematic review was conducted by searching MEDLINE, Embase, PsycINFO and the Cochrane Database of Systematic Reviews. Abstracts of retrieved studies were reviewed and those meeting the inclusion criteria were selected for full-text review. The quality of evidence presented in the included studies was assessed using the Medical Education Research Study Quality Instrument (MERSQI), where a score of 14 or more of 18 indicates high-quality evidence. RESULTS: Of 1809 studies identified, 37 used standard-setting methodology for assessment of procedural skill. Of these, 24 used participant-centred and 13 employed item-centred methods. Thirty studies took place in a simulated environment, and seven in a clinical setting. The included studies assessed residents (26 of 37), fellows (6 of 37) and staff physicians (17 of 37). Seventeen articles achieved a MERSQI score of 14 or more of 18, whereas 20 did not meet this mark. CONCLUSION: Absolute standard-setting methodologies can be used to establish cut-offs for procedural skill assessments.
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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 | 0.046 | 0.178 |
| Meta-epidemiology (narrow) | 0.003 | 0.002 |
| Meta-epidemiology (broad) | 0.012 | 0.011 |
| Bibliometrics | 0.019 | 0.016 |
| Science and technology studies | 0.001 | 0.002 |
| Scholarly communication | 0.005 | 0.006 |
| Open science | 0.004 | 0.003 |
| Research integrity | 0.003 | 0.002 |
| Insufficient payload (model declined to judge) | 0.006 | 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".