Recommendations for Improving Ergonomics in Cleft Palate and Lip Surgery: A Scoping Review
Bibliographic record
Abstract
Introduction: Craniofacial and cleft surgeries demand high precision, often leading to significant ergonomic challenges for surgeons. This review highlights critical recommendations for mitigating musculoskeletal disorders and enhancing surgeon well-being through equipment optimization, posture adjustments, and innovative technologies. Methods: To conduct this scoping review, a search of the literature yielded 18 studies which discuss ergonomics in the realm of cleft lip and palate surgery. Results: Eighteen studies were reviewed. Investigated ergonomic strategies in cleft lip and palate surgery encompassed headlights, loupes, microscopes, robots, videoscopes, endoscopes, exoscopes, positioning techniques, breaks, exercise, workshops, intubation methods, operating room design, and training programs. Most studies were classified as low risk of bias. Robotic systems and advanced visualization tools such as videoscopes and exoscopes demonstrated significant ergonomic benefits by improving surgeon posture, reducing musculoskeletal strain, and enhancing surgical precision. Microscopes and loupes offered improved magnification and comfort but were still associated with some reported musculoskeletal symptoms. Strategies including micro-breaks, exercise, ergonomic positioning, and optimized operating room setup showed potential to reduce physical discomfort during surgery. However, formal ergonomics education remains scarce in surgical training despite its recognized importance. Conclusion: Appropriate tools and positioning decrease the incidence of musculoskeletal disorders among cleft lip and palate surgeons. Further studies should focus on the outcomes of implementing such programs.
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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 | 0.019 | 0.055 |
| Meta-epidemiology (narrow) | 0.002 | 0.001 |
| Meta-epidemiology (broad) | 0.004 | 0.005 |
| Bibliometrics | 0.015 | 0.010 |
| Science and technology studies | 0.001 | 0.001 |
| Scholarly communication | 0.004 | 0.007 |
| Open science | 0.003 | 0.003 |
| Research integrity | 0.005 | 0.003 |
| Insufficient payload (model declined to judge) | 0.010 | 0.002 |
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".