Respiratory disease associated with solid biomass fuel exposure in rural women and children: systematic review and meta-analysis
Bibliographic record
Abstract
BACKGROUND: Numerous studies with varying associations between domestic use of solid biomass fuels (wood, dung, crop residue, charcoal) and respiratory diseases have been reported. OBJECTIVE: To present the current data systematically associating use of biomass fuels with respiratory outcomes in rural women and children. METHODS: Systematic searches were conducted in 13 electronic databases. Data were abstracted from original articles that satisfied selection criteria for meta-analyses. Publication bias and heterogeneity of samples were tested. Studies with common diagnoses were analysed using random-effect models. RESULTS: A total of 2717 studies were identified. Fifty-one studies were selected for data extraction and 25 studies were suitable for meta-analysis. The overall pooled ORs indicate significant associations with acute respiratory infection in children (OR 3.53, 95% CI 1.94 to 6.43), chronic bronchitis in women (OR 2.52, 95% CI 1.88 to 3.38) and chronic obstructive pulmonary disease in women (OR 2.40, 95% CI 1.47 to 3.93). In contrast, no significant association with asthma in children or women was noted. CONCLUSION: Biomass fuel exposure is associated with diverse respiratory diseases in rural populations. Concerted efforts in improving stove design and lowering exposure to smoke emission may reduce respiratory disease associated with biomass fuel 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 | 0.008 | 0.023 |
| Meta-epidemiology (narrow) | 0.002 | 0.001 |
| Meta-epidemiology (broad) | 0.015 | 0.023 |
| Bibliometrics | 0.007 | 0.008 |
| Science and technology studies | 0.000 | 0.001 |
| Scholarly communication | 0.002 | 0.001 |
| Open science | 0.002 | 0.001 |
| Research integrity | 0.002 | 0.001 |
| Insufficient payload (model declined to judge) | 0.003 | 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".