Factors Associated With Musculoskeletal Pain Among Hair Transplant Surgeons: Analyses of Survey Data and Review of the Literature
Bibliographic record
Abstract
BACKGROUND: The prevalence of work-related musculoskeletal disorders (WRMD) is increasing among all surgical specialties. OBJECTIVE: Results of a cross-sectional survey of hair transplant surgeons were analyzed, with the aims to (1) determine the prevalence of WRMD, (2) assess risk factors associated with musculoskeletal (MSK) symptoms, and (3) identify mitigation measures. MATERIALS AND METHODS: A survey pertaining to demographics, MSK-related symptoms and its impacts, and pain mitigation measures taken, if any, were distributed to 834 hair transplant surgeons. Risk factors associated with pain severity were assessed using linear regression. RESULTS: Overall, 78.5% (73 of 93) respondents had experienced pain when performing surgery. Musculoskeletal symptoms were most severe in the neck, followed by upper/lower back, and extremities. Number of grafts performed per session of follicular unit extraction positively correlated with pain severity; female surgeons and surgeons aged >71 years were at higher risk. A majority expressed concern that WRMD may limit their career and agreed to a need for improved workplace education. Strength training and ergonomic improvements of surgical procedure were not commonly adopted. CONCLUSION: In sum, WRMD can be debilitating in health care professionals. Workplace ergonomic adjustments and physical exercise programs may be warranted to better mitigate MSK symptoms.
Fetched live from OpenAlex and de-inverted. Abstracts are not stored in this database: the inverted indexes are 8.6 GB of the frame’s 9.3 GB of text, and the host has 13 GB free.
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 | 0.005 |
| Meta-epidemiology (narrow) | 0.001 | 0.000 |
| Meta-epidemiology (broad) | 0.002 | 0.002 |
| Bibliometrics | 0.007 | 0.009 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.001 | 0.001 |
| Open science | 0.001 | 0.001 |
| Research integrity | 0.001 | 0.000 |
| 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".