Work-related musculoskeletal pain among orthopaedic surgeons: A systematic literature search and narrative synthesis
Bibliographic record
Abstract
Background: Performing surgery is associated with physical demands and musculoskeletal hazards. Orthopaedic surgery is a particularly physically demanding surgical discipline. We aimed to systematically examine the literature characterizing the prevalence and nature of work-related musculoskeletal (MSK) pain among orthopaedic surgeons. Methods: Systematic search and narrative synthesis methodology of studies on MSK pain among orthopaedic surgeons was conducted in MEDLINE, Embase, and CINAHL. Data extraction of study characteristics was performed and further analyzed for prevalence, pain outcome measures, and anatomical location of MSK pain. This review is registered in PROSPERO CRD420250650511. Results: 25 studies met our inclusion criteria. 14 studies were published since 2019, with no articles before 1995. 11 papers studied surgeons in the USA and the remaining from other countries. The range of overall MSK pain prevalence was 51.7-97.0 %. 11 studies reported on pain in 1-2 anatomical regions, while 9 studies reported on >3 regions. MSK pain was most frequently reported in the lower back (prevalence of 17.1-77.0 %); neck (10.4-74.4 %); and shoulder (12.8-66.7 %). 13 studies determined MSK pain via author-made or unspecified instruments while 12 papers used validated tools for surveying MSK pain. 17 studies specified a time period in which MSK pain reports were captured. Conclusion: Orthopaedic surgeons report a high frequency of MSK pain, in the lower back, neck and shoulder regions. There was considerable heterogeneity of research methods and outcome measures utilized. Further research is needed to better understand the role of preventive measures and the potential influence of MSK pain on surgeon occupational function, and the orthopaedic surgeon workforce.
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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 | 0.018 | 0.071 |
| Meta-epidemiology (narrow) | 0.002 | 0.002 |
| Meta-epidemiology (broad) | 0.010 | 0.009 |
| Bibliometrics | 0.022 | 0.017 |
| Science and technology studies | 0.001 | 0.002 |
| Scholarly communication | 0.005 | 0.004 |
| Open science | 0.002 | 0.005 |
| Research integrity | 0.003 | 0.002 |
| Insufficient payload (model declined to judge) | 0.008 | 0.001 |
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".