Abstract 13292: Rural-Urban Differences in Mortality Rate of Myocardial Infarction and Heart Failure: A Systematic Review and Meta-Analysis
Bibliographic record
Abstract
Introduction: Studies indicate that living in a rural area may be associated with worse health outcomes. However, it is unclear whether rurality is associated with mortality in patients with acute myocardial infarction (AMI) and heart failure (HF). This systematic review and meta-analysis aimed to evaluate rural-urban differences in mortality rate after AMI or HF. Methods: A systematic search was conducted for studies published till May 1, 2022 in PubMed, Embase and CENTRAL. Included studies compared mortality rates after hospital admission for AMI or HF in rural versus urban areas. Follow up ranged between 30 days and 3 years. Adjusted Odds ratios (aORs) were pooled with a random-effects model. Results: Six cohort studies were identified, which included 785,156 patients (128,990 in rural vs. 656,166 in urban) with AMI and 1,159,000 patients (192,749 in rural vs. 966,251 in urban) with HF. Compared with urban, patients admitted with AMI in rural areas had higher mortality rates (16% vs 14%, aOR 1.12, 95% Confidence Interval (CI) 1.05-1.20; p =0.0003, I 2 =75%). Compared with urban, patients admitted with HF in rural areas had higher mortality rates (21% vs 18%, aOR 1.19, 95% CI 1.03-1.38; p =0.02, I 2 =90%). (Figure) Conclusions: Among patients with AMI or HF, living in rural areas is associated with an increased risk of mortality. Clinical and policy efforts are needed in order to reduce disparities in rural health.
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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 | 0.028 |
| Meta-epidemiology (narrow) | 0.002 | 0.001 |
| Meta-epidemiology (broad) | 0.017 | 0.029 |
| Bibliometrics | 0.007 | 0.008 |
| Science and technology studies | 0.001 | 0.001 |
| Scholarly communication | 0.003 | 0.002 |
| 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".