Novel Surgical Skill Evaluation with Reference to Two-handed Coordination.
Bibliographic record
Abstract
INTRODUCTION: We evaluated the differences in instrument manipulation skills between expert laparoscopic surgeons and novices. METHODS: Six expert surgeons who had performed more than 500 laparoscopic surgeries and one skilled instructor at Kyushu University Training Center for Minimally Invasive Surgery, and 20 medical students who had experienced no laparoscopic surgery were enrolled. A new skill assessment task was designed using zippers on an unstable, mobile platform in a box trainer. The examinees were asked to close the zippers, while trying to avoid moving the platform. The path lengths of the tips of the instruments and of the platform were measured, and the performance time was also recorded. Surgical skill score was calculated from the correlation between the path lengths of the instruments and that of the platform, in addition to the performance time. RESULTS: The path lengths of the tips of both instruments and of the platform were significantly shorter in the experts than in the novices (all p < 0.05). The performance time was also significantly shorter for experts than novices (p < 0.05). The surgical skill score was significantly higher for experts than novices (p < 0.01). CONCLUSION: The differences in the instrument manipulation skills between expert laparoscopic surgeons and novices could therefore be evaluated using our surgical skill scoring system.
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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 | 0.001 | 0.004 |
| Meta-epidemiology (narrow) | 0.001 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.001 | 0.000 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.000 | 0.001 |
| Open science | 0.000 | 0.001 |
| Research integrity | 0.000 | 0.000 |
| Insufficient payload (model declined to judge) | 0.004 | 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".