Physical Supporting Devices as Interventions to Reduce Muscular Load of Surgeons in the Operating Room
Bibliographic record
Abstract
INTRODUCTION: Heavy muscle load during operations, caused by static and awkward postures, contributes to the discomfort of surgeons, and imperils surgical quality. We reviewed the supporting devices available to assist surgeons in the operating room and anticipated that physical support devices would help reduce occupational injuries among surgeons and improve surgical performance. METHODS: A systematic literature review was completed. Papers on supporting devices for intraoperative stress reduction were included. Supported body parts and the impact of these devices on the surgeons' performance were extracted from the 21 selected papers. RESULTS: Among the 21 devices introduced, eleven targeted on the upper extremities, 5 targeted on the lower extremities, and 5 were ergonomic chairs. Nine devices were tested in the operating room, 10 in a lab setting with simulated tasks, and 2 were still in development. The data from 7 studies did not show a significant improvement in stress reduction or surgical quality. With 2 devices still in the development phase, the remaining 12 papers showed promising results. DISCUSSION: Although some of the devices were still in testing, most of the research teams believed that physical supporting devices can be useful in reducing muscle load, relieving discomfort, and improving surgical performance intraoperatively.
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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 | 0.008 |
| Meta-epidemiology (narrow) | 0.001 | 0.000 |
| Meta-epidemiology (broad) | 0.003 | 0.003 |
| Bibliometrics | 0.004 | 0.003 |
| Science and technology studies | 0.000 | 0.001 |
| Scholarly communication | 0.002 | 0.001 |
| Open science | 0.001 | 0.001 |
| Research integrity | 0.002 | 0.001 |
| Insufficient payload (model declined to judge) | 0.005 | 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".