Setting Performance Standards for Technical and Nontechnical Competence in General Surgery
Bibliographic record
Abstract
OBJECTIVES: The objectives of this study were to (1) create a technical and nontechnical performance standard for the laparoscopic cholecystectomy, (2) assess the classification accuracy and (3) credibility of these standards, (4) determine a trainees' ability to meet both standards concurrently, and (5) delineate factors that predict standard acquisition. BACKGROUND: Scores on performance assessments are difficult to interpret in the absence of established standards. METHODS: Trained raters observed General Surgery residents performing laparoscopic cholecystectomies using the Objective Structured Assessment of Technical Skill (OSATS) and the Objective Structured Assessment of Non-Technical Skills (OSANTS) instruments, while as also providing a global competent/noncompetent decision for each performance. The global decision was used to divide the trainees into 2 contrasting groups and the OSATS or OSANTS scores were graphed per group to determine the performance standard. Parametric statistics were used to determine classification accuracy and concurrent standard acquisition, receiver operator characteristic (ROC) curves were used to delineate predictive factors. RESULTS: Thirty-six trainees were observed 101 times. The technical standard was an OSATS of 21.04/35.00 and the nontechnical standard an OSANTS of 22.49/35.00. Applying these standards, competent/noncompetent trainees could be discriminated in 94% of technical and 95% of nontechnical performances (P < 0.001). A 21% discordance between technically and nontechnically competent trainees was identified (P < 0.001). ROC analysis demonstrated case experience and trainee level were both able to predict achieving the standards with an area under the curve (AUC) between 0.83 and 0.96 (P < 0.001). CONCLUSIONS: The present study presents defensible standards for technical and nontechnical performance. Such standards are imperative to implementing summative assessments into surgical training.
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How this classification was reachedexpand
Full frame distilled prediction
Teacher imitationNot calibrated prevalence, not ground truth. Human validation pending. Learned from the 10,348 direct Codex labels and 10,348 direct Gemma labels. Candidate is the union of thresholded teacher heads; consensus is their intersection. These outputs are machine_predicted_unvalidated and are not human labels or direct frontier model labels.
Codex and Gemma teacher scores by category
| Category | Codex | Gemma |
|---|---|---|
| Metaresearch | 0.002 | 0.001 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.000 | 0.000 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.000 | 0.000 |
| Open science | 0.000 | 0.000 |
| Research integrity | 0.000 | 0.000 |
| Insufficient payload (model declined to judge) | 0.000 | 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 teacher head, 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".