Refining How We Define Laparoscopic Expertise
Bibliographic record
Abstract
BACKGROUND: Traditional stratification of expertise in laparoscopic simulation assigns participants to novice, intermediate, or expert groups based on case numbers. We hypothesized that expert video assessment might refine this discrimination of psychomotor expertise, especially in light of new measurable parameters. MATERIALS AND METHODS: One hundred five participants performed a defined intracorporeal suturing task in the pediatric laparoscopic surgery simulator armed with force-sensing capabilities. Participants were stratified into novice, intermediate, and expert groups via three classification schemes: (1) number of complex laparoscopic cases, (2) self-declared level of expertise, and (3) average expert rating of participants' videos. Precision, time to task completion, and force analysis parameters (FAP = total, maximum and mean forces in three axes) were compared using one-way analysis of variance tests. P < .05 was considered significant. RESULTS: Participants stratified on the basis of case numbers and on the basis of self-declared level of expertise had statistically significant differences in time to task completion, but no significant difference in FAP. When participants were restratified according to expert assessment of their video performance, time to task completion as well as total and mean forces in X, Y, and Z axes allowed discrimination between novices, intermediates, and experts, thus establishing construct validity for the latter. Precision did not allow discrimination in any stratification scheme. CONCLUSION: Compared with traditional stratification, video assessment allows refined discrimination of psychomotor expertise within a simulator. Assessment of FAP may become a relevant tool for teaching and assessing laparoscopic skills.
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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 | 0.023 | 0.080 |
| Meta-epidemiology (narrow) | 0.001 | 0.000 |
| Meta-epidemiology (broad) | 0.001 | 0.001 |
| Bibliometrics | 0.004 | 0.001 |
| Science and technology studies | 0.001 | 0.009 |
| Scholarly communication | 0.004 | 0.008 |
| Open science | 0.002 | 0.004 |
| Research integrity | 0.001 | 0.003 |
| Insufficient payload (model declined to judge) | 0.003 | 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".