Ultraviolet Exposure and Photokeratitis Complaints among Informal Welding Workers in Depok, West Java, Indonesia
Bibliographic record
Abstract
One of the photokeratitis risk factors is acute reversible radiation of ultraviolet (UV) rays, which injure the cornea's epithelial tissue. Informal welding workers are susceptible populations to UV rays exposure. This study aimed to confirm the influence of UV radiation exposure on photokeratitis complaints in welding workers in Cimanggis, Depok, West Java. A cross-sectional study was conducted from February to June 2019 and used to select 100 welding workers purposively. A semi-structured questionnaire was used to determine photokeratitis complaints, age, education level, eye protection, safety knowledge, and work period; the UV radiation measured by A UV meter. Data were analyzed using a logistic regression test. We found the proportion of photokeratitis to be 84.0%, with 76.0% of UV radiation exceeding the Threshold Limit Values (TLV). The logistic regression test showed a significant effect of UV radiation on photokeratitis after controlling confounding variables (education level, eye protection, safety knowledge, and welding distance) (p-value = 0.006; AdjOR = 7.236; 95% CI: 1.74–30.07). It can be concluded that UV radiation, more than TLV, constitutes the primary risk factor for photokeratitis complaints. Risks for photokeratitis were influenced by low education level, poor eye protection, limited safety knowledge, and welding distance ≤ 45 cm.
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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 | 0.000 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.000 | 0.000 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.000 | 0.000 |
| Open science | 0.000 | 0.000 |
| Research integrity | 0.000 | 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".