Work-Related Musculoskeletal Injuries in Plastic Surgeons in the United States, Canada, and Norway
Bibliographic record
Abstract
BACKGROUND: Musculoskeletal injuries are more common among surgeons than among the general population. However, little is known about these types of injuries among plastic surgeons specifically. The authors' goals were to evaluate the prevalence, nature, causes, and potential solutions of these musculoskeletal injuries among plastic surgeons in three different countries: the United States, Canada, and Norway. METHODS: A survey was e-mailed to plastic surgeons in the United States, Canada, and Norway, soliciting their demographics, practice description, history of musculoskeletal issues, potential causes of these symptoms, and proposed suggestions to address these injuries. The prevalence of various musculoskeletal symptoms was calculated, and predictors of these symptoms were evaluated using multivariate logistic regression. RESULTS: The survey was sent to 3314 plastic surgeons, with 865 responses (response rate, 26.1 percent); 78.3 percent of plastic surgeons had musculoskeletal symptoms, most commonly in the neck, shoulders, and lower back. U.S. surgeons were significantly more likely to have musculoskeletal symptoms than Norwegian surgeons (79.5 percent versus 69.3 percent; p < 0.05); 6.7 percent of all respondents required surgical intervention for their symptoms. The most common causative factors were long surgery duration, tissue retraction, and prolonged neck flexion. The most common solutions cited were core-strengthening exercises, stretching exercises, and frequent adjustment of table height during surgery. CONCLUSIONS: Plastic surgeons are at high risk for work-related musculoskeletal injuries. Ergonomic principles can be applied in the operating room to decrease the incidence and severity of those injuries, and to avoid downstream sequelae, including the need for surgery.
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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 | 0.002 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.002 | 0.003 |
| Science and technology studies | 0.002 | 0.001 |
| Scholarly communication | 0.001 | 0.000 |
| Open science | 0.000 | 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".