Interventions to Reduce Musculoskeletal Pain in Ophthalmologists: A Systematic Review
Bibliographic record
Abstract
Background: Musculoskeletal (MSK) discomfort is a significant occupational hazard for eye care professionals, including ophthalmologists, who report high rates of MSK discomfort. This systematic review investigated the impact of various interventions, such as regular exercise, posture-correcting aids, and surgical heads-up displays, on reducing MSK pain in the operating room. Methods: This review was reported following PRISMA guidelines and was prospectively registered in the PROSPERO database (CRD42024559189). A systematic literature search was conducted of Embase, MEDLINE, and Web of Science from inception to 2024. Included studies were categorized as exercise modifications, equipment modifications, or training aids. All MSK pain-related outcomes from any time point were extracted. Risk of bias was assessed using the Murad tool, the Cochrane risk-of-bias tool for randomized trials (RoB 2), and the Risk Of Bias In Non-Randomized Studies-of Interventions (ROBINS-I) tool. Results: The systematic search strategy identified 2276 studies, of which 53 qualified for full-text screening with 13 resultant studies including 712 eyecare specialists. Physical activity was found unanimously to reduce MSK pain, with favourable evidence for the utilization of posture-correcting aids. There was mixed—but mostly favourable—evidence for the use of surgical heads-up displays. Conclusions: Exercise modifications, such as yoga and regular exercise; equipment modification with heads-up displays during surgery; and training aids for posture correction were shown to be beneficial for MSK-related pain among ophthalmologists. Future studies should strive to improve the certainty of evidence on ergonomics-related interventions for ophthalmologists, which will better support practice and guideline development.
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 | 0.008 | 0.037 |
| Meta-epidemiology (narrow) | 0.002 | 0.001 |
| Meta-epidemiology (broad) | 0.010 | 0.009 |
| Bibliometrics | 0.007 | 0.007 |
| Science and technology studies | 0.001 | 0.001 |
| Scholarly communication | 0.003 | 0.002 |
| Open science | 0.002 | 0.001 |
| Research integrity | 0.002 | 0.002 |
| Insufficient payload (model declined to judge) | 0.006 | 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".