Effect of Exposure to Cats and Dogs on the Risk of Asthma and Allergic Rhinitis: A Systematic Review and Meta-analysis
Bibliographic record
Abstract
BACKGROUND: Controversies persist regarding whether exposure to cat or dog increases the risk of asthma and allergic rhinitis. OBJECTIVE: This meta-analysis aimed to assess the associations between exposure to cats or dogs and the development of asthma and allergic rhinitis. METHODS: A systematic review was performed to identify case-control and cohort studies before May 2019, evaluating the association between exposure to cats and dogs and the risk of asthma and rhinitis. The risk of bias was assessed using the Newcastle-Ottawa Scale. The odds ratios (ORs) and risk ratios (RRs) were pooled for case-control and cohort studies, respectively. Subgroup analyses were performed on prespecified study-level characteristics. RESULTS: The meta-analysis of 34 cohort studies showed a protective role of exposure to cats [RR: 0.88, 95% confidence interval (CI): 0.77-0.99] or dogs (RR: 0.85, 95% CI: 0.73-0.97) in the development of asthma. The subgroup analysis of birth cohort (RR: 0.74, 95% CI: 0.56-0.93) and children population (RR: 0.83, 95% CI: 0.70-0.96) also suggested a favorable role of exposure to dogs in the development of asthma. Pooled evidence from 13 case-control studies indicated no significant impact of cats (OR: 1.66, 95% CI: 0.39-2.94) and dogs (OR: 1.22, 95% CI: 0.92-1.52) on the development of asthma. A pooled analysis of five cohort studies showed a favorable effect of exposure to cats (RR: 0.60, 95% CI: 0.33-0.86) or dogs (RR: 0.68, 95% CI 0.44-0.90) on the development of allergic rhinitis. CONCLUSION: The findings indicated a protective effect of exposure to cats and dogs, especially ownership, on the development of asthma and allergic rhinitis.
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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 | 0.009 | 0.025 |
| Meta-epidemiology (narrow) | 0.003 | 0.001 |
| Meta-epidemiology (broad) | 0.017 | 0.037 |
| Bibliometrics | 0.006 | 0.006 |
| Science and technology studies | 0.001 | 0.001 |
| Scholarly communication | 0.003 | 0.001 |
| Open science | 0.002 | 0.001 |
| Research integrity | 0.002 | 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".