Developing Criteria for Proficiency-Based Training of Surgical Technical Skills Using Simulation: Changes in Performances as a Function of Training Year
Bibliographic record
Abstract
BACKGROUND: Proficiency-based residency training programs can be more efficient than the current duration-based formats. For their successful implementation, appropriate proficiency criteria must be developed. The objective of this study was to investigate the relationship between technical skill performances assessed using computer- and expert-based methods and training year. An assumption was that asymptotes in performance as a function of training year can be used to set the proficiency level for a technical skill, so the value at which the asymptote occurs can be labeled as the proficiency criteria. STUDY DESIGN: Thirty-eight general surgery residents performed one-handed knot tying on bench-top simulators at two levels of difficulty: superficial and deep. Motion-efficiency measures and expert-based measures were used to evaluate performance. Total number of operations (ie, surgical volume) that each trainee participated in during residency was also acquired. RESULTS: On the superficial model, asymptotes were observed at year 1 for motion-efficiency and year 3 for expert-based measures. On the deep model, asymptotes were observed at year 2 for motion-efficiency and year 4 for expert-based measures. CONCLUSIONS: The data demonstrate the challenges associated with defining technical skills proficiency criteria. Different asymptotes were observed for the two assessment methods and neither covaried substantially with surgical volume. These data suggest that this asymptote approach in defining proficiency criteria can be suitable for development of proficiency-based residency training programs. The sensitivity of this approach to the type of assessment method and to the functional difficulty of the simulators used for assessment must be considered.
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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 | 0.014 | 0.062 |
| 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 | 0.001 |
| Scholarly communication | 0.001 | 0.001 |
| Open science | 0.001 | 0.001 |
| Research integrity | 0.001 | 0.001 |
| Insufficient payload (model declined to judge) | 0.001 | 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".