Postoperative Coronary Artery Bypass Graft Readmissions in Rural, Remote, and Northern Communities: A Case-Control Study Focused on the Social Determinants of Health
Bibliographic record
Abstract
Background: People living in rural, remote, and northern communities (RRNCs) are at higher risk for hospital readmissions following coronary artery bypass graft (CABG). The aim of this study was to identify factors associated with hospital readmission post-CABG in Canadian RRNCs, including social determinants of health (SDOH). Methods: In this case-control study, we reviewed 44 patient charts readmitted within 30 days post-CABG to one RRNC hospital and the charts of 44 patients not readmitted to this hospital. Results: Logistic regression analysis revealed that readmission was associated with history of myocardial infarction (OR 2.52; 95% CI 1.49–4.24), fewer days waiting for surgery (OR 1.02; 95% CI 1.00–1.03), living in a larger population centre (OR 0.18; 95% CI 0.07–0.51), shorter distance (km) to the hospital where the surgery took place (OR 1.01; 95% CI 1.01– 1.02), and need for community care post-CABG (OR 14.97; 95% CI 4.03–55.65). Conclusion: Readmission post-CABG was correlated with access to acute and community healthcare. Nursing Implications: There is a need to integrate the SDOH in pre- and post-surgical education, discharge planning, and specialized community services in RRNCs. Keywords: case-control study, rural remote and northern communities, social determinants of health, coronary artery bypass graft, post-operative readmissions
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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 | 0.001 | 0.002 |
| Meta-epidemiology (narrow) | 0.001 | 0.001 |
| Meta-epidemiology (broad) | 0.001 | 0.001 |
| Bibliometrics | 0.001 | 0.002 |
| Science and technology studies | 0.003 | 0.001 |
| Scholarly communication | 0.001 | 0.000 |
| Open science | 0.001 | 0.001 |
| Research integrity | 0.001 | 0.001 |
| Insufficient payload (model declined to judge) | 0.001 | 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".