Eye health risks associated with unclean fuel: a meta-analysis and systematic review
Bibliographic record
Abstract
Purpose This study comprehensively examined the correlation between unclean cooking fuels (UCF) and ocular health, covering diverse eye conditions such as cataracts, visual impairments, and ocular discomfort. Methods According to MOOSE and PRISMA guidelines, a meta-analysis and systematic review was conducted on 28 studies from 3 databases. Literature quality was assessed using the Newcastle-Ottawa Scale. Heterogeneity among articles was gaged with the I2 statistic, sensitivity analysis used ‘leave-one-out test’, and publication bias was evaluated using Egger, Begg tests, and funnel plot analysis. Results The study evidenced a significant association between UCF exposure and cataracts [OR 2.29, 95% CI (1.24, 4.23)], visual impairments [OR 1.70, 95% CI (1.45, 2.00)], and eye diseases/symptoms [OR 2.03, 95% CI (1.25, 3.29)]. However, no correlation was found between UCF exposure and glaucoma or elevated intraocular pressure [OR 0.96, 95% CI (0.84, 1.10), n = 2]. Subgroup analysis revealed that UCF cooking had an impact on nuclear cataracts [OR 1.98, 95% CI (1.67, 2.33), n = 4]. But not on cortical cataracts [OR 1.25, 95% CI (0.98, 1.60), n = 3]. Additionally, UCF exposure was linked to severe visual impairments like night blindness [OR 2.03, 95% CI (1.00, 4.96)], blindness [OR 1.43, 95% CI (1.32, 1.55)], and specific ocular symptoms such as tearing while cooking (OR = 3.20), eye irritation (OR = 2.58), and red eyes (OR = 2.03). Conclusion UCF cooking had significant impact on ocular health, notably on eye symptoms, cataracts, and visual impairments. UCF exposure presented demographic inequalities in cataract prevalence, while eye symptoms can serve as a reliable self-assessment of UCF exposure.
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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 | 0.014 | 0.034 |
| Meta-epidemiology (narrow) | 0.003 | 0.002 |
| Meta-epidemiology (broad) | 0.018 | 0.040 |
| Bibliometrics | 0.009 | 0.008 |
| 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".