Do Micropauses Prevent Surgeon's Fatigue and Loss of Accuracy Associated With Prolonged Surgery? An Experimental Prospective Study
Bibliographic record
Abstract
OBJECTIVE: This prospective experimental study evaluates the effectiveness of micropauses (MPs) to prevent muscular fatigue and its deleterious effect on surgeons during prolonged surgical procedures. BACKGROUND: Operating is a hazard for surgeon's health. Beyond acute injuries and blood-borne infections, back and neck pain is a poorly recognized factor causing chronic ailment in more than half the surgeons surveyed. MP, a 20-second break every 20 minutes, is an accepted strategy used widely in the workplace. METHODS: We designed a crossover experimental study. Sixteen surgeons were tested 3 times: once in a control situation before any surgery (CTL) and twice after a prolonged, reproducible operation (at least 2 hours), 1 of these with formal MP (WMP) the other without (WOMP). Muscular fatigue was tested by holding a 2.5-kg weight as long as possible with a stretched arm. Accuracy was evaluated with a device, measuring the mistakes made when following a predetermined path on a board. Finally, discomfort was measured by visual analog scale. RESULTS: We found a statistically and more importantly clinically significant difference between the CTL and WOMP groups in all 3 tests. MPs prevented completely or almost completely the effects of fatigue associated with surgery [accuracy (No. errors) CTL: 1.1, WOMP: 7.7, WMP: 1.7; fatigue (seconds) CTL: 137, WOMP: 92, WMP: 142]. CONCLUSIONS: Surgical procedures are associated with significant muscular fatigue that can be measured simply and which has a direct effect on comfort and surgical accuracy. More important, this effect is completely or almost completely prevented by MPs.
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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 | 0.002 | 0.003 |
| Meta-epidemiology (narrow) | 0.001 | 0.000 |
| Meta-epidemiology (broad) | 0.001 | 0.001 |
| Bibliometrics | 0.000 | 0.000 |
| Science and technology studies | 0.000 | 0.001 |
| Scholarly communication | 0.000 | 0.001 |
| Open science | 0.001 | 0.000 |
| Research integrity | 0.001 | 0.001 |
| Insufficient payload (model declined to judge) | 0.004 | 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".