Ergonomics in Craniofacial Surgery: Can We Do Better? A Quality Improvement Study
Bibliographic record
Abstract
OBJECTIVE: Many plastic surgeons experience musculoskeletal strain and injury from poor ergonomics during surgery. This is associated with reduced performance, shortened careers, and decreased quality of life. This study compared the ergonomics of the conventional operating table headrest and the Mayfield headrest in craniofacial surgery. METHODS: A prospective cohort study of patients undergoing craniofacial operations between November 20, 2022 and April 26, 2023, within a single craniofacial surgeon's practice. The authors obtained data on the total duration of the operation and Rapid Entire Body Assessment (REBA) scores for the primary surgeon and assistant. RESULTS: Four operations (mean: 147 ± 60.9 min) were included in the regular headrest group, and 8 in the Mayfield headrest group (mean: 61±53.4 min). Four hundred fifty-five regular headrest time points and 851 Mayfield time points were recorded. Eight hundred thirty-five regular headrest time points and 538 Mayfield time points were recorded. The mean REBA score for the regular headrest was 5.79 ± 1.9, which was higher than the Mayfield (5.01 ± 2.0; P < 0.0001). Subgroup analysis showed the mean REBA score for the primary surgeon (5.89 ± 2.0) was higher than the assist (5.48 ± 1.6) in the regular headrest group ( P < 0.0001), whereas the converse was true for the Mayfield headrest (primary surgeon: 4.67 ± 1.8, assist: 5.65 ± 2.15, P < 0.0001). CONCLUSIONS: Ergonomic scores were better using the Mayfield headrest than the regular headrest. The primary surgeon scored better with the Mayfield headrest, whereas the assists had better scores with the regular headrest.
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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 | 0.008 | 0.020 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.001 | 0.001 |
| Bibliometrics | 0.001 | 0.003 |
| Science and technology studies | 0.001 | 0.001 |
| Scholarly communication | 0.001 | 0.001 |
| Open science | 0.001 | 0.001 |
| Research integrity | 0.001 | 0.001 |
| Insufficient payload (model declined to judge) | 0.002 | 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".