The Effect of Biomass Burning on Respiratory Symptoms and Lung Function in Rural Mexican Women
Bibliographic record
Abstract
BACKGROUND: The use of biomass as a cooking fuel is commonplace in developing countries and has been associated with chronic bronchitis and obstructive airway disease. METHODS: A cross-sectional survey was done in the village of Solis, close to Mexico City. Lifelong nonsmoking women 38 yr of age or older (n=841) completed a questionnaire on respiratory symptoms and illnesses and on cooking fuel use, and performed spirometry in their homes. Particulate matter concentration was measured with a nephelometer in the kitchen for 1 h, while the subject was cooking. RESULTS: The peak indoor concentration of particulate matter (PM10, particles with a diameter of 10 microm or less) often exceeded 2 mg/m3. Compared with those cooking with gas, current use of a stove burning biomass fuel was associated with increased reporting of phlegm (27 vs. 9%) and reduced FEV1/FVC (79.9 vs. 82.8%). Levels of FEV1 were 81 ml lower and cough was more common (odds ratio, 1.7; 95% confidence interval, 1.0-2.8) in women from homes with higher PM10 concentrations. All women found with moderate airflow obstruction (Global Initiative for Chronic Obstructive Lung Disease stage II and above) were cooking with biomass stoves. CONCLUSION: Women cooking with biomass fuels have increased respiratory symptoms and a slight average reduction in lung function compared with those cooking with gas.
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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 | 0.002 |
| 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) | 0.002 | 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".