How Do Thresholds of Principle and Preference Influence Surgeon Assessments of Learner Performance?
Bibliographic record
Abstract
OBJECTIVE: The present study asks whether intraoperative principles are shared among faculty in a single residency program and explores how surgeons' individual thresholds between principles and preferences might influence assessment. BACKGROUND: Surgical education continues to face significant challenges in the implementation of intraoperative assessment. Competency-based medical education assumes the possibility of a shared standard of competence, but intersurgeon variation is prevalent and, at times, valued in surgical education. Such procedural variation may pose problems for assessment. METHODS: An entire surgical division (n = 11) was recruited to participate in video-guided interviews. Each surgeon assessed intraoperative performance in 8 video clips from a single laparoscopic radical left nephrectomy performed by a senior learner (>PGY5). Interviews were audio recorded, transcribed, and analyzed using the constant comparative method of grounded theory. RESULTS: Surgeons' responses revealed 5 shared generic principles: choosing the right plane, knowing what comes next, recognizing normal and abnormal, making safe progress, and handling tools and tissues appropriately. The surgeons, however, disagreed both on whether a particular performance upheld a principle and on how the performance could improve. This variation subsequently shaped their reported assessment of the learner's performance. CONCLUSIONS: The findings of the present study provide the first empirical evidence to suggest that surgeons' attitudes toward their own procedural variations may be an important influence on the subjectivity of intraoperative assessment in surgical education. Assessment based on intraoperative entrustment may harness such subjectivity for the purpose of implementing competency-based surgical education.
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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 | 0.018 | 0.133 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.001 | 0.001 |
| Science and technology studies | 0.001 | 0.002 |
| Scholarly communication | 0.003 | 0.001 |
| Open science | 0.001 | 0.001 |
| Research integrity | 0.001 | 0.001 |
| 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".