Occupational injuries and burn out among orthopedic oncology surgeons
Bibliographic record
Abstract
BACKGROUND: Orthopedic oncology surgeons commonly engage in prolonged and complex surgical procedures. These types of surgeries increase the risk of physical and psychological stressors, which may in turn make these physicians prone to work-related occupational injuries. AIM: The aim of this study was to explore in orthopedic oncologists, the prevalence of work-related physical injuries and psychological disturbances. METHODS: A modified version of the physical discomfort survey was developed to assess occupational injuries among orthopedic oncology surgeon members of the Musculoskeletal Tumor Society, the Canadian Orthopedic Oncology Society and European Musculoskeletal Oncology Societies. The survey was sent by email, and it explored musculoskeletal complaints, psychological disturbances, treatment required for these complaints and the requirement of time off work. RESULTS: A total of 67 surgeon responses were collected. A high number of orthopedic oncologists (84%) reported an occupational injury. Low back pain (39%) was the most prevalent musculoskeletal condition, followed by lumbar disk herniation (16%), shoulder tendinitis (15%) and lateral epicondylitis (13%). Of the cohort, 46% required surgery and 31% required time off work due to their injury. Thirty-three respondents reported a psychological disorder. Burnout (27%), anxiety (20%) and insomnia (20%) were the most commonly reported. Time required off work due to injury was associated with old age and years in practice. CONCLUSION: Orthopedic oncology surgeons report a high prevalence of work-related disorders. Lower back related injury and burnout were the most reported disorders. Improving operative room ergonomics and prevention of stress related to the work environment should be areas to explore in upcoming research.
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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 | 0.001 |
| Science and technology studies | 0.001 | 0.000 |
| Scholarly communication | 0.000 | 0.000 |
| Open science | 0.000 | 0.001 |
| Research integrity | 0.000 | 0.000 |
| Insufficient payload (model declined to judge) | 0.003 | 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".