Abstract MP65: Fish Consumption and Acute Coronary Syndrome: A Meta-Analysis
Bibliographic record
Abstract
Background: While findings on omega-3 supplements in cardiovascular diseases (CVD) are controversial, various studies suggest that fish consumption may be beneficial to cardiovascular health and reduce the risk of acute coronary syndrome (ACS). The purpose of this study was to investigate the association between fish consumption and ACS by conducting a dose-response meta-analysis. Methods: We conducted a literature search of Medline and Embase databases from 1966 to June 2013 for prospective cohort and case-control studies that evaluated the association between fish consumption and ACS among general populations without CVD history. Additional studies were identified via hand search of references of relevant articles. Estimates of relative risk (RR) were pooled using random effects model. Sex and age effects were also evaluated. Results: Our search retrieved 11 prospective cohort and 8 case-control studies, totaling 408,305 participants. Among prospective cohort studies, the highest category of fish consumption, i.e. ≥4 times per week, was associated with the greatest risk reduction in ACS (RR 0.79, 95% CI 0.70-0.89). Among case-control studies, fish consumption also appeared to reduce ACS risk (RR 0.76, 95% CI 0.67-0.87 for 1 to <2 times per week). In dose-response analysis, each additional 100 g serving of fish per week was associated with a 5% reduced risk (RR per serving 0.95, 95% CI 0.92-0.97). Subgroup analysis and meta-regression suggested that the risk reduction did not differ across sex or age groups. Conclusions: Our meta-analysis demonstrated that there is an inverse association between fish consumption and ACS risk. Fish consumption appears beneficial in the primary prevention of ACS and higher consumption was associated with higher protection.
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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 | 0.012 | 0.024 |
| Meta-epidemiology (narrow) | 0.003 | 0.001 |
| Meta-epidemiology (broad) | 0.013 | 0.055 |
| Bibliometrics | 0.006 | 0.006 |
| Science and technology studies | 0.001 | 0.001 |
| Scholarly communication | 0.003 | 0.002 |
| Open science | 0.002 | 0.002 |
| Research integrity | 0.002 | 0.003 |
| Insufficient payload (model declined to judge) | 0.007 | 0.001 |
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".