Survey of reported eye injuries from handheld laser devices in Canada
Bibliographic record
Abstract
BACKGROUND: Unprotected exposure to handheld lasers can cause temporary or permanent vision loss depending on the laser classification. OBJECTIVE: To evaluate the occurrence of, and details associated with, reported eye injuries resulting from handheld lasers. METHODS: A 14-item questionnaire developed by Health Canada was distributed by the Canadian Ophthalmological Society and the Canadian Association of Optometrists to their respective members. RESULTS: Questionnaire data were available from 909 respondents (263 ophthalmologists; 646 optometrists). Response rates were 23.1% and 12.7%, respectively. Validated data were available from 903 respondents, where 157 (17.4%) reported encountering at least 1 eye injury from a handheld laser. A total of 318 eye injuries were reported with an annual increase of 34.4% (95% CI 21.6%-48.7%, p < 0.0001) between 2013 and 2017. When respondents reported on only their most severe case, 77 (53.5%) reported vision loss that ranged from minor to severe, which persisted for more than 6 months in 42.9% of the cases. Another 59 (41.3%) noted the presence of retinal damage. The prevalence of eye injuries from handheld lasers was higher for males (82.5%) than females (14.0%), more frequent among those under the age of 50 years, and occurred predominately as a result of exposure from another person (67.6%) versus self-induced (26.1%) (p < 0.0001). CONCLUSIONS: Although this pilot study permits insight into the potential prevalence of injuries resulting from exposure to handheld laser devices in Canada, the results are not nationally representative. These findings support additional surveillance activities that may inform risk assessment and potential risk management strategies.
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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 | 0.000 | 0.002 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.001 | 0.003 |
| Science and technology studies | 0.001 | 0.000 |
| Scholarly communication | 0.001 | 0.000 |
| Open science | 0.001 | 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".