Types of ocular injury and their antecedent factors: A systematic review and meta‐analysis
Bibliographic record
Abstract
BACKGROUND: Ocular injuries are an important workplace hazard that can lead to vision loss, decreased functioning, and socioeconomic costs. The aim of this systematic review is to identify types of occupational ocular injuries and examine factors associated with these injuries. METHODS: Four health sciences databases (Ovid Medline, Embase, PsycINFO, and CINAHL) were reviewed to identify evidence pertaining to occupational ocular injuries. This systematic review was registered with PROSPERO (registration number: CRD42018089876) and followed the Preferred Reporting Items for Systematic Reviews and Meta-Analyses (PRISMA). The PICO (Population/Intervention/Comparison/Outcome) tool was used to support, structure, and improve our search strategy. RESULTS: Overall, 12 studies with quantitative Critical Appraisal Skills Programme grading scores were assessed in a systematic review and meta-analysis of ocular injuries in the workplace. The systematic review identified four main factors associated with occupational ocular injury: (a) use of eye protection at the time of the ocular injury, (b) being male, (c) exposure to biological or chemical occupational hazards, and (d) risk-taking behavior. CONCLUSIONS: Differences in risk between countries of origin, occupational sectors, and dates of publication, suggest likely differences or changes in safety procedures. We recommend that employers ensure that safety equipment is tailored to the protection of their specific occupational hazards, and that employees are adhering to safety protocols.
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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 | 0.017 | 0.049 |
| Meta-epidemiology (narrow) | 0.003 | 0.002 |
| Meta-epidemiology (broad) | 0.015 | 0.033 |
| Bibliometrics | 0.009 | 0.011 |
| Science and technology studies | 0.001 | 0.001 |
| Scholarly communication | 0.004 | 0.002 |
| Open science | 0.002 | 0.002 |
| Research integrity | 0.002 | 0.002 |
| Insufficient payload (model declined to judge) | 0.004 | 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".