Respiratory health effects of exposure to environmental tobacco smoke
Bibliographic record
Abstract
Tobacco smoke is a major component of indoor air pollution. Exposure to environmental tobacco smoke (ETS) is prevalent worldwide despite growing awareness of its adverse health effects on non-smokers. ETS contains the same toxic substances as identified in mainstream tobacco smoke. Cotinine (a metabolite of nicotine) can be measured in urine and serum of non-smokers exposed to ETS and reflects the degree of exposure. In children, exposure to ETS leads to reduced lung function, increased risk of lower respiratory tract illnesses, acute exacerbation of asthma resulting in hospitalization, increased prevalence of non-allergic bronchial hyperresponsiveness, increased risk for sudden infant death syndrome (SIDS) and possibly increased risk for asthma. Exposure to ETS is responsible for excess cost to the family's financial resources and demands on health services. In adults, exposure to ETS is associated with increased risk of lung cancer, particularly in those with high exposure and acute and chronic respiratory symptoms that improve after the cessation of exposure. Healthcare providers should advocate for non-smokers' rights in the community and support legislation to limit tobacco exposure.
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 | 0.000 | 0.001 |
| Meta-epidemiology (narrow) | 0.001 | 0.000 |
| Meta-epidemiology (broad) | 0.001 | 0.001 |
| Bibliometrics | 0.001 | 0.001 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.001 | 0.001 |
| Open science | 0.001 | 0.001 |
| Research integrity | 0.001 | 0.001 |
| Insufficient payload (model declined to judge) | 0.005 | 0.003 |
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".