Combined exposure to dog and indoor pollution: incident asthma in a high-risk birth cohort
Bibliographic record
Abstract
The impact of single exposures on asthma development is better understood than the effect of multiple exposures. The objective of the present study was to evaluate the effect of combined early exposure to dog allergen (Can-f1) plus indoor nitrogen dioxide (NO₂) or environmental tobacco smoke (ETS) on asthma and bronchial hyperreactivity (BHR) in a high-risk birth cohort. We also aimed to assess atopy's impact on the effects of these exposures. Peri-birth ETS exposure was measured using cord blood cotinine (CCot). During year 1, atopy, NO₂, Can-f1, and urinary cotinine (UCot) were measured. At 7 yrs of age, 380 children were assessed for asthma and BHR. Exposure effects were determined using stepwise multiple linear regression. Co-exposure to elevated Can-f1 and NO₂, or Can-f1 and ETS (CCot), increased risk for asthma, relative to having neither such exposure (OR 4.8 (95% CI 1.1-21.5) and 2.7 (1.1-7.1), respectively); similar risks resulted when substituting dog ownership for allergen. Atopy increased asthma and BHR risk associated with several exposures; notably, atopy with elevated UCot, relative to atopy without such exposure, increased risk of BHR (OR 3.1 (95% CI 1.1-8.6)). In a high-risk birth cohort, early co-exposure to Can-f1 and NO₂ or ETS increased the risk of incident asthma. Atopy increased the risk of asthma and BHR associated with ETS.
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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 | 0.001 | 0.001 |
| Meta-epidemiology (narrow) | 0.000 | 0.001 |
| Meta-epidemiology (broad) | 0.000 | 0.001 |
| Bibliometrics | 0.001 | 0.001 |
| Science and technology studies | 0.001 | 0.000 |
| Scholarly communication | 0.001 | 0.000 |
| Open science | 0.000 | 0.001 |
| Research integrity | 0.001 | 0.001 |
| Insufficient payload (model declined to judge) | 0.001 | 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".