A modest protective association between pet ownership and cardiovascular diseases: A systematic review and meta-analysis
Bibliographic record
Abstract
PURPOSE: Investigate the relationship between pet ownership and cardiovascular (CV) outcomes. METHODS: We searched the PubMed, Ovid EMBASE, Cumulative Index to Nursing and Allied Health Literature, Cochrane Database of Systematic Reviews, and Cochrane Central Register of Controlled Trials databases up to August 2018. Eligible publications examining the association between pet ownership and all-cause and CV mortality (primary outcomes) and risks of cardiovascular disease (CVD), myocardial infarction (MI), and stroke (secondary outcomes) were included. We used the Newcastle-Ottawa Scale to assess the quality of the articles. RESULTS: We included 12 studies, involving 488,986 participants (52.3% female, mean age 56.1 years), in our systematic review. The mean follow-up duration was 8.7 ± 6.3 years. Pet ownership had no association with adjusted all-cause mortality (odds ratio, OR = 1.01, 95% confidence interval, CI [0.94, 1.08], I2 = 76%), adjusted CV mortality (OR = 0.87, 95% CI [0.75, 1.00], I2 = 72%), or risk of cardiovascular disease (CVD) (OR = 0.87, 95% CI [0.72, 1.05], I2 = 73%), myocardial infarction (MI) (OR = 0.99, 95% CI [0.97, 1.01], I2 = 0%), or stroke (OR = 0.99, 95% CI [0.98, 1.01], I2 = 0%). However, subgroup analysis showed that pet ownership was associated with a lower adjusted CV mortality in the general population (OR = 0.93, 95% CI [0.86, 0.99], I2 = 27%) than in CVD patients. In patients with established CVD, pet ownership was associated with a lower adjusted CVD risk (OR = 0.71, 95% CI [0.60, 0.84], I2 = 0%). CONCLUSION: Pet ownership is not associated with adjusted all-cause or CV mortality, or risk of CVD, MI, or stroke, but it is associated with a lower adjusted CV mortality in the general population and a lower CVD risk in patients with established CVD.
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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 | 0.014 | 0.044 |
| Meta-epidemiology (narrow) | 0.003 | 0.002 |
| Meta-epidemiology (broad) | 0.019 | 0.031 |
| Bibliometrics | 0.006 | 0.006 |
| Science and technology studies | 0.001 | 0.001 |
| Scholarly communication | 0.004 | 0.003 |
| Open science | 0.002 | 0.002 |
| 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".