ERS/ATS workshop report on respiratory health effects of household air pollution
Bibliographic record
Abstract
Exposure to household air pollution (HAP) from solid fuel combustion affects almost half of the world population. Adverse respiratory outcomes such as respiratory infections, impaired lung growth and chronic obstructive pulmonary disease have been linked to HAP exposure. Solid fuel smoke is a heterogeneous mixture of various gases and particulates. Cell culture and animal studies with controlled exposure conditions and genetic homogeneity provide important insights into HAP mechanisms. Impaired bacterial phagocytosis in exposed human alveolar macrophages possibly mediates several HAP-related health effects. Lung pathological findings in HAP-exposed individuals demonstrate greater small airways fibrosis and less emphysema compared with cigarette smokers. Field studies using questionnaires, air pollution monitoring and/or biomarkers are needed to better establish human risks. Some, but not all, studies suggest that improving cookstove efficiency or venting emissions may be associated with reduced respiratory symptoms, lung function decline in women and severe pneumonia in children. Current studies focus on fuel switching, stove technology replacements or upgrades and air filter devices. Several governments have initiated major programmes to accelerate the upgrade from solid fuels to clean fuels, particularly liquid petroleum gas, which provides research opportunities for the respiratory health community.
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 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 | 0.015 | 0.004 |
| Meta-epidemiology (narrow) | 0.002 | 0.001 |
| Meta-epidemiology (broad) | 0.001 | 0.003 |
| Bibliometrics | 0.002 | 0.001 |
| Science and technology studies | 0.001 | 0.000 |
| Scholarly communication | 0.002 | 0.001 |
| Open science | 0.004 | 0.003 |
| Research integrity | 0.009 | 0.004 |
| Insufficient payload (model declined to judge) | 0.026 | 0.019 |
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".