Prevalence of laser beam exposure and associated injuries.
Bibliographic record
Abstract
BACKGROUND: An increasing number of consumer laser products are available to Canadians, many being purchased from online retailers. Of particular concern are high-powered, handheld laser devices. This study was conducted to assess the impact of this influx of laser products on the number of laser-associated injuries in Canada. DATA AND METHODS: The rapid response component of the 2014 Canadian Community Health Survey collected data from 19,765 Canadians on the prevalence of laser product exposure and usage, the type of laser product used, and the incidence of eye or skin injuries. RESULTS: Approximately half of Canadians (48.1%) reported using or being exposed to a laser product in the previous 12 months. The highest laser product usage or exposure was among those with university education (58.6%) and those with higher income categories (p ⟨ 0.0001). The highest prevalence of exposure or usage involved laser scanners (38.7%), laser pointers (11.1%) and lasers for entertainment (9.7%). Overall, about 1% of Canadians reported discomfort or injury involving a laser product in the past 12 months. Over half the injuries (59.1%) occurred to the eyes. Most of the injuries (74.9%) resulted from someone else's use of the device. The majority of the reported injuries were caused by lasers for cosmetic treatment or laser pointers. DISCUSSION: Despite the prevalence of laser product usage and exposure among Canadians, a low percentage of respondents reported injuries. This is likely because most laser devices are low-powered and typically do not represent a hazard. Nonetheless, efforts to increase awareness of laser product risks may be beneficial given the findings of this study.
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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 | 0.003 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.002 | 0.004 |
| Science and technology studies | 0.002 | 0.001 |
| Scholarly communication | 0.001 | 0.000 |
| Open science | 0.001 | 0.001 |
| Research integrity | 0.000 | 0.001 |
| Insufficient payload (model declined to judge) | 0.008 | 0.001 |
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".