The computerized objective assessment of surgical skills: Considerations for counting the number of movements
Bibliographic record
Abstract
Motion capture and analysis techniques are emerging in the surgical education and surgical education research literature as viable ways to augment the assessment of technical skills. In particular, these methods provide an opportunity to reveal objective information about the efficiency of surgical procedures, above and beyond the accuracy of procedural outcomes. One assessment that is very prevalent in the literature are counts of the number of movements a surgeon makes in completing a technical performance. In this commentary, the number of movements metric is explored from kinesiology and engineering perspectives; two disciplines that have contributed heavily to the development of rigorous motion analysis methods. Furthermore, the assumption that skill efficiency improves linearly as a learner progresses along the continuum of expertise is challenged. While movement efficiency does certainly improve, this assumption does not necessarily capture the way that learners flexibly prioritize particular aspects of performance in the intermediate stages of skill learning. By way of this commentary, important a priori decisions that should proceed effective motion capture and analysis are highlighted, a call for the standardization of procedures is made, and an opportunity to better understand the way that computerized movement analysis techniques may contribute (or be detrimental) to competency constructs in surgical education and assessment is realized.
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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 | 0.037 | 0.183 |
| Meta-epidemiology (narrow) | 0.001 | 0.000 |
| Meta-epidemiology (broad) | 0.001 | 0.001 |
| Bibliometrics | 0.003 | 0.002 |
| Science and technology studies | 0.001 | 0.018 |
| Scholarly communication | 0.007 | 0.006 |
| Open science | 0.003 | 0.002 |
| Research integrity | 0.007 | 0.007 |
| Insufficient payload (model declined to judge) | 0.002 | 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".