A Survey of Occupational Musculoskeletal Symptoms Among Canadian Plastic Surgeons and Trainees
Bibliographic record
Abstract
Purpose: Despite the advances of modern operating rooms, surgeons often experience work environments that rival those of industrial workers with regard to the risk of musculoskeletal (MSK) injuries or disorders. Such injuries may result in loss of hours, decreased surgical volume, or premature retirement. This study aimed to investigate the prevalence and impact of MSK injuries among Canadian plastic surgeons and trainees. Methods: A cross-sectional, online survey was disseminated among Canadian plastic surgeons, defined as those registered as members of the Canadian Society of Plastic Surgeons, the Royal College of Physicians and Surgeons of Canada, or Plastic Surgery Residents. Results: This survey was disseminated to 604 Canadian plastic surgeons, fellows, and residents, of whom 139 responded (response rate 23.0%). Of the responders, 49.6% were male, 23.0% were >35 years of age, and 46.1% had been in practice for >10 years. The majority (72.7%) of respondents endorsed experiencing MSK symptoms after operating. Moreover, 18.7% of respondents felt their MSK symptoms had direct consequences on their performance as a surgeon. When MSK symptoms were reported to department heads, system change was only seen 44.4% of the time. Unsurprisingly, neck (76.2%), back (72.2%), and shoulders (48.5%) were the areas of pain most reported. Exercise was not shown to significantly reduce the impact of MSK symptoms resulting from operating ( P = .06). Conclusions: Musculoskeletal symptoms are common among plastic surgeons and directly impact the performance of a large proportion of surgeons. Besides traditional efforts to reinforce good posture while operating, best practice policies and operating room optimization with regard to ergonomics are warranted.
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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 | 0.003 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.001 | 0.003 |
| Science and technology studies | 0.001 | 0.000 |
| Scholarly communication | 0.000 | 0.000 |
| Open science | 0.001 | 0.001 |
| Research integrity | 0.000 | 0.000 |
| Insufficient payload (model declined to judge) | 0.002 | 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".