Exposure to furry pets and the risk of asthma and allergic rhinitis: a meta‐analysis
Bibliographic record
Abstract
BACKGROUND: Exposure to pets has been implicated as a risk factor for asthma. However, this relation has been difficult to assess in individual studies because of the large potential of selection bias. We sought to examine the association between exposure to furry pets and asthma and allergic rhinitis by means of a meta-analysis. METHODS: We retrieved studies published in any language by searching systematically Medline (1966-March 2007), Embase, LILACS and ISI Proceedings computerized databases, and by examining manually the references of the original articles and reviews retrieved. We included cohort and case-control studies reporting relative risk estimates and confidence intervals of exposure to cats, dogs and unspecified furry animals and subsequent asthma and allergic rhinitis. We excluded cross-sectional studies and those studies that did not measure exposure but rather sensitization to pets. RESULTS: Thirty-two studies were included. For asthma, the pooled relative risk related to dog exposure was 1.14 (95% CI 1.01-1.29), that related to exposure to any furry pet was 1.39 (95% CI 1.00-1.95). Among cohort studies, exposure to cats yielded a relative risk of 0.72 (95% CI 0.55-0.93). For rhinitis, the pooled relative risk of exposure to any furry pet was 0.79 (95% CI 0.68-0.93). CONCLUSIONS: Exposure to cats exerts a slight preventive effect on asthma, an effect that is more pronounced in cohort studies. On the contrary, exposure to dogs increases slightly the risk of asthma. Exposure to furry pets of undermined type is not conclusive. More studies with exact measurement of exposure are needed to elucidate the role of pet exposures in atopic diseases.
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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 | 0.011 | 0.022 |
| Meta-epidemiology (narrow) | 0.003 | 0.002 |
| Meta-epidemiology (broad) | 0.013 | 0.049 |
| Bibliometrics | 0.005 | 0.005 |
| Science and technology studies | 0.001 | 0.001 |
| Scholarly communication | 0.003 | 0.001 |
| Open science | 0.002 | 0.001 |
| Research integrity | 0.003 | 0.002 |
| Insufficient payload (model declined to judge) | 0.004 | 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".