Comparative Assessment of Ergonomic Experience with Heads-Up Display and Conventional Surgical Microscope in the Operating Room
Bibliographic record
Abstract
Purpose: Musculoskeletal pain issues are prevalent in ophthalmic surgeons and can impact surgeon well-being and productivity. Heads-up displays (HUD) can improve upon conventional microscopes by reducing ergonomic stress. This study compared ergonomic outcomes between HUD and a conventional optical microscope in the operating room, as reported by ophthalmic surgeons in the US. Methods: An online questionnaire was distributed to a sample of surgeons who had experience operating with HUD. The questionnaire captured surgeon-specific variables, the validated Nordic Musculoskeletal Questionnaire, and custom questions to compare HUD and conventional microscope. A multivariable model was built to identify variables that were likely to predict improvement in pain-related issues. Results: Analysis was conducted on 64 surgeons (37 posterior-segment, 25 anterior-segment, and two mixed) with a mean 14.9 years of practice and 2.3 years using HUD. Most surgeons agreed or strongly agreed that HUD reduced the severity (64%) and frequency (63%) of pain and discomfort, improved posture (73%), and improved overall comfort (77%). Of respondents who experienced headaches, or pain and discomfort during operation, 12 (44%) reported their headaches improved and 45 (82%) reported feeling less pain and discomfort since they started using HUD. The multivariable model indicated the odds of reporting an improvement in pain since introducing the HUD in the operating room were 5.12-times greater for those who used HUD in > 50% of their cases ( P =0.029). Conclusion: This study indicates that heads-up display may be an important tool for wellness in the operating room as it can benefit ophthalmic surgeons across several ergonomic measures. Keywords: heads-up display, microscope, ergonomic, musculoskeletal disorders, ophthalmology, surgery
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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 | 0.001 | 0.007 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.001 | 0.000 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.000 | 0.000 |
| Open science | 0.000 | 0.001 |
| Research integrity | 0.000 | 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".