Effect of exposure to biomass smoke from cooking fuel types and eye disorders in women from hilly and plain regions of Nepal
Bibliographic record
Abstract
BACKGROUND/AIM: To study the association between exposure to biomass smoke from cooking fuels andi cataract, visual acuity and ocular symptoms in women. METHODS: We conducted a community-based cross-sectional study among women (≥20 years and without a previous diagnosis of cataract, ocular trauma or diabetes or those taking steroids) from hilly and plain regions of Nepal. Eligible participants received an interview and a comprehensive eye assessment (cataract development, visual acuity test and ocular symptoms). Participants' data on demographics, cooking fuel type and duration of use, and cooking habits were collected. We addressed potential confounders using the propensity score and other risk factors for ocular diseases through regression analysis. RESULTS: Of 784 participants, 30.6% used clean fuel (liquefied petroleum gas, methane, electricity) as their primary current fuel, and the remaining 69.4% used biomass fuels. Thirty-nine per cent of the total participants had cataracts-about twofold higher in those who currently used biomass fuel compared with those who used clean fuel (OR=2.27; 95% CI 1.09 to 4.77) and over threefold higher in those who always used biomass. Similarly, the nuclear cataract was twofold higher in the current biomass user group compared with the clean fuel user group (OR=2.53; 95% CI 1.18-5.42) and over threefold higher among those who always used biomass. A higher proportion of women using biomass had impaired vision, reported more ocular symptoms compared with those using clean fuel. Severe impaired vision and blindness were only present in biomass fuel users. However, the differences were only statistically significant for symptoms such as redness, burning sensation, a complaint of pain in the eye and tear in the eyes. CONCLUSIONS: Cataract was more prevalent in women using biomass for cooking compared with those using clean fuel.
Fetched live from OpenAlex and de-inverted. Abstracts are not stored in this database: the inverted indexes are 8.6 GB of the frame’s 9.3 GB of text, and the host has 13 GB free.
How this classification was reachedexpand
Full frame distilled prediction
Teacher imitationNot calibrated prevalence, not ground truth. Human validation pending. Learned from the 10,348 direct Codex labels and 10,348 direct Gemma labels. Candidate is the union of thresholded teacher heads; consensus is their intersection. These outputs are machine_predicted_unvalidated and are not human labels or direct frontier model labels.
Codex and Gemma teacher scores by category
| 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) | 0.000 | 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 teacher head, 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".