Prevalence of musculoskeletal pain among gynecologic surgeons performing laparoscopic procedures: A systematic review and meta‐analysis
Bibliographic record
Abstract
Abstract Objective Musculoskeletal discomfort is associated with repetitive movements and constrained body positions. The current meta‐analysis was performed to determine the global prevalence of musculoskeletal symptoms among gynecologic surgeons who perform laparoscopy. Methods Sources included Embase, MEDLINE, PubMed, CINAHL, Web of Science Core Collection, Cochrane Central Register of Controlled Clinical Trials, and Google Scholar. Articles published between 1980 and 2022 were considered. Studies that assessed self‐reported musculoskeletal symptoms were included. Relevant data were extracted and tabulated. Results Twelve studies met the inclusion criteria. In a pooled sample of 1619 surgeons, the estimated prevalence of musculoskeletal symptoms was 82% (95% confidence interval [CI], 70%–89%;I2, 92%). Female sex was a risk factor, as identified by a pooled odds ratio of 4.64 (95% CI, 2.63–8.19;I2, 0%) compared with male surgeons. Among surgeons who reported musculoskeletal symptoms, 30% (95% CI, 14%–52%;I2, 95%) sought treatment and 3% (95% CI, 2%–6%;I2, 0%) required work hour modifications. Conclusion The current meta‐analysis provides preliminary evidence of a high prevalence of musculoskeletal symptoms among gynecologic laparoscopic surgeons. Future research is needed to explore the underlying risk factors and interventional strategies to mitigate this risk.
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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 | 0.013 | 0.038 |
| Meta-epidemiology (narrow) | 0.003 | 0.002 |
| Meta-epidemiology (broad) | 0.016 | 0.034 |
| Bibliometrics | 0.008 | 0.009 |
| Science and technology studies | 0.001 | 0.001 |
| Scholarly communication | 0.004 | 0.002 |
| Open science | 0.002 | 0.001 |
| Research integrity | 0.002 | 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".