Prevalence of musculoskeletal disorders among orthopedic trauma surgeons: an OTA survey
Bibliographic record
Abstract
BACKGROUND: Occupational injuries and hazards have gained increased attention in the surgical community in general and in the orthopedic literature specifically. The aim of this study was to assess prevalence and characteristics of musculoskeletal disorders among orthopedic trauma surgeons and the impact of these injuries on the surgeons' practices. METHODS: We sent a modified version of the physical discomfort survey to surgeon members of the Orthopaedic Trauma Association (OTA) via email. Data were collected and descriptive statistics were analyzed. RESULTS: A total of 86 surgeons completed the survey during the period of data collection; 84.9% were men, more than half were 45 years or older and 40.6% were in practice for 10 years or more. More than 66% of respondents reported a musculoskeletal disorder that was related to work; the most common was low back pain (29.3%). The number of body regions involved and disorders diagnosed was associated with increasing age and number of years in practice (p = 0.033). Time off work owing to these disorders was associated with working in a private setting (p = 0.045) and working in more than 1 institute (p = 0.009). CONCLUSION: To our knowledge, our study is the first to report a high percentage of orthopedic trauma surgeons sustaining occupational injuries some time in their careers. The high cost of management and rehabilitation of these injuries in addition to the related number of missed work days indicate the need for increased awareness and implementation of preventive measures.
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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 | 0.002 | 0.003 |
| Science and technology studies | 0.001 | 0.000 |
| Scholarly communication | 0.001 | 0.001 |
| 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".