Socioeconomic status and cardiovascular disease : Universal inequities and the challenges that lie ahead
Bibliographic record
Abstract
Socioeconomic status (SES) has been shown to be a strong predictor of cardiovascular outcomes worldwide. The reasons for SES-related outcome disparities are unclear. While studies have not been able to entirely attribute SES-related outcome disparities to variations in accessing medical care, the relationship between SES, quality of care, and outcomes remain important given that practice patterns are modifiable. In contrast to the United States, the goals of Canada's health care system are to improve the health status of all of its citizens and to ensure access to care on the basis of need rather than income. Therefore, theoretically, SES should not be a determinant of cardiovascular access and outcomes in Canada. Yet, evidence suggests otherwise. This article reviews the relationship between SES, access to invasive cardiac procedures, and outcomes after acute myocardial infarction in Canada. We discuss reasons for SES-related outcome disparities, and hypothesize that SES-related access differences in Canada reflect underlying clinical uncertainty over treatment benefits, rather than discrimination.
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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 | 0.003 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.001 | 0.001 |
| Science and technology studies | 0.001 | 0.003 |
| Scholarly communication | 0.002 | 0.001 |
| Open science | 0.000 | 0.002 |
| Research integrity | 0.001 | 0.001 |
| Insufficient payload (model declined to judge) | 0.002 | 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".