Reply: Work-Related Musculoskeletal Injuries in Plastic Surgeons in the United States, Canada, and Norway
Bibliographic record
Abstract
Sir: We would like to thank Drs. Ando, Fuse, and Yamamoto for their letter regarding our recently published study entitled “Work-Related Musculoskeletal Injuries in Plastic Surgeons in the United States, Canada, and Norway.”1 In their letter, the authors describe a modified microscope with a screen that allows both the surgeon and the assistant to look forward instead of down while performing microsurgical procedures. In our study, we found that microsurgery was one of the three plastic surgical procedures most likely to exacerbate musculoskeletal symptoms. We also found that long surgery duration and prolonged neck flexion were two of the three maneuvers most likely to trigger musculoskeletal symptoms. Therefore, the novel idea presented by Drs. Ando, Fuse, and Yamamoto is of great potential benefit, because it allows microsurgeons to avoid prolonged neck flexion. Perhaps even more harmful than neck flexion is forward head posture, which microsurgeons tend to adopt to reach the eyepiece of the microscope, which is usually located over the patient. Forward head posture causes significant strain on the neck: for every inch of forward head positioning, the stress exerted by the head on the neck increases by 10 lb.2 Because the authors use the microscope at various magnifications for the entire surgical procedure, including flap elevation, the camera also obviates the need for surgical loupes, which are thought to contribute to neck pain. Although several studies have failed to demonstrate a significant association between modern lightweight loupes and musculoskeletal symptoms,3,4 the authors’ idea may have great benefit nonetheless. Some questions regarding this new technology remain: How steep is the learning curve, in terms of hand-eye coordination, for the microsurgeon who is accustomed to operating with a conventional microscope? Does the new system allow the assistant to actively participate in the operation, or does it diminish the operating experience of the trainee? A study evaluating surgical outcomes, operating times, and the experience of both the surgeon and the trainee with the new system would be a welcome addition to both the microsurgery and ergonomics fields. DISCLOSURE Dr. Janis has served as a consultant for LifeCell, Bard, Daiichi Sankyo, Pacira, and Allergan within the last 12 months prior to submission of this article but has no active conflicts of interest, and receives royalties from Thieme Publishing. Drs. Khansa, Westvik, Lista, and Khansa have no relevant financial disclosures. Dr. Ahmad receives royalties from Thieme Publishing. Ibrahim Khansa, M.D.Division of Plastic and Maxillofacial SurgeryChildren’s Hospital Los AngelesLos Angeles, Calif. Lara Khansa, Ph.D.Department of Business Information TechnologyPamplin College of BusinessVirginia TechBlacksburg, Va. Tormod S. Westvik, M.D.Division of Plastic SurgeryTelemark HospitalSkien, Norway Jamil Ahmad, M.D.Frank Lista, M.D.Division of Plastic and Reconstructive SurgeryUniversity of TorontoToronto, Ontario, Canada Jeffrey E. Janis, M.D.Department of Plastic SurgeryThe Ohio State University Wexner Medical CenterColumbus, Ohio
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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 | 0.002 | 0.021 |
| Meta-epidemiology (narrow) | 0.001 | 0.001 |
| Meta-epidemiology (broad) | 0.001 | 0.001 |
| Bibliometrics | 0.001 | 0.001 |
| Science and technology studies | 0.003 | 0.002 |
| Scholarly communication | 0.002 | 0.003 |
| Open science | 0.002 | 0.001 |
| Research integrity | 0.023 | 0.016 |
| Insufficient payload (model declined to judge) | 0.005 | 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".