Survey of the use of laser protective eyewear among international retina specialists: a European vitreoretinal society study
Bibliographic record
Abstract
OBJECTIVE: To report the trends for the use of eye protection methods during retinal laser in clinic and operating room. METHODS AND ANALYSIS: Retrospective analysis of a 14-item survey questionnaire submitted to the European Vitreoretinal Society members. RESULTS: Responses from 630 members were analysed. Most of the respondents practised in Europe (52.7%), followed by North America (21.0%). The majority of respondents had laser filters in the microscope for the operating surgeon (92.1%), or used protective goggles (6.8%). Only 38.9% of respondents indicated that auxiliary staff in the operative room used protective goggles during laser treatment. Three-dimensional retina viewing system was used by only 22.5% of respondents, of those, 34.5% reported use of laser protection goggles by the operating surgeon. Rates of laser protection by auxiliary staff were 62.9% for indirect laser and 60.8% for slit lamp laser. We found a higher rate for use of laser protection by auxiliary staff in North America-based practices for endolaser (p<0.00001), laser indirect ophthalmoscope (p<0.00001) and slit lamp laser (p=0.00033) compared with the rest of the world. CONCLUSION: The use of laser protection methods is routinely adopted by the physicians in the operating room and clinic, but less so by their assisting or auxiliary staff.
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 | 0.001 | 0.003 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.001 | 0.001 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.000 | 0.001 |
| Open science | 0.000 | 0.000 |
| Research integrity | 0.001 | 0.000 |
| Insufficient payload (model declined to judge) | 0.001 | 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".