Prevalence, Risk Factors, and Impacts of Musculoskeletal Injuries Among Plastic Surgeons: A Systematic Review and Meta-Analysis
Bibliographic record
Abstract
Background: Poor ergonomics may lead to musculoskeletal (MSK) injuries in surgeons, prompting practice modification, early retirement, and errors. We hypothesized that each plastic surgery subspecialty faces unique ergonomic challenges. We sought to identify the prevalence, risk factors, and impact of MSK injuries among plastic surgeons by subspecialty. Methods: In this systematic review and meta-analysis (CRD420251026702), MEDLINE, Embase, CINAHL, Web of Science, and Scopus were searched from inception to November 9, 2024, for studies reporting on injury prevalence among plastic surgeons. Methodological quality was assessed using the Joanna Briggs Institute Checklist for Prevalence Studies. The primary outcome was the literature-pooled MSK injury prevalence. Secondarily, we reviewed ergonomic risk factors and impacts. Pairwise and nonpairwise meta-analyses and random effects meta-analyses were conducted for dichotomous and continuous outcomes, respectively. Results: Fifteen studies were included, encompassing 3313 plastic surgeons. Very low-certainty evidence suggested that 72.0% reported MSK injury (95% CI [63.4, 79.3]), with the greatest prevalence in aesthetic surgeons (84.3%; 95% CI [76.0, 90.1]) and lowest among craniofacial surgeons (69.4%; 95% CI [52.8, 82.1]). Symptoms mostly occurred in the neck (47.0%), lower back (37.0%), and upper back (32.0%). Consequently, 38.6% worried about disability, 10.1% took time off, and 5.9% decreased their caseload. Practice duration, caseload, and age were risk factors. Conclusion: Ergonomic-related injuries varied among subspecialties, affecting the neck (craniofacial, oculoplastic, and microsurgeons), forearm/wrist/hands (hand surgeons), and lower back (aesthetic surgeons). Tailored ergonomic education programs are needed to protect the health and well-being of plastic surgeons.
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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 | 0.017 | 0.044 |
| Meta-epidemiology (narrow) | 0.003 | 0.002 |
| Meta-epidemiology (broad) | 0.018 | 0.044 |
| Bibliometrics | 0.010 | 0.011 |
| Science and technology studies | 0.001 | 0.001 |
| Scholarly communication | 0.004 | 0.002 |
| Open science | 0.003 | 0.002 |
| Research integrity | 0.003 | 0.002 |
| 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".