Cardiovascular Medication Use and Long‐Term Outcomes of First Nations and Non–First Nations Patients Following Diagnostic Angiography: A Retrospective Cohort Study
Bibliographic record
Abstract
Background In Canada, First Nations (FN) people are at greater risk of mortality than the general population following index angiography. This disparity has not been investigated while considering guideline-recommended cardiovascular medication use. Methods and Results Retrospective analysis of administrative health data investigated patterns of medication dispensation during the first year after index angiography among patients in Manitoba, Canada. Medication possession ratios (MPRs) reflecting the percentage of days in which medications were supplied were calculated separately for β-blockers, angiotensin-converting enzyme inhibitors, statins, and antiplatelets (clopidogrel). Patients were assigned to 1 of 4 categories: (1) not dispensed (0% MPR), (2) low (1-39% MPR), (3) intermediate (40-79% MPR), (4) high (≥80% MPR). Cox regression models that adjusted for MPR categories were used to explore the association between FN patients and both 5-year all-cause mortality and cardiovascular mortality. FN patients were less likely to have an intermediate MPR (odds ratio: 0.75; 95% CI, 0.57-0.99) or a high MPR (odds ratio: 0.64; 95% CI, 0.50-0.81) for statin medications than non-FN patients. FN patients also had higher adjusted risks of all-cause and cardiovascular mortality than non-FN patients (hazard ratio, all-cause: 1.54 [95% CI, 1.25-1.89]; cardiovascular: 1.62 [95% CI, 1.16-2.25]). Conclusions FN status was independently associated with intermediate and high MPRs for statins during the first year following index angiography among patients with known ischemic heart disease. Differences in MPR categories did not explain the disparity in all-cause and cardiovascular mortality between the 2 populations. Reduction of cardiovascular disparities may be best addressed using primary prevention strategies that include decolonizing policies and practices.
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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) | 0.000 | 0.001 |
| Meta-epidemiology (broad) | 0.000 | 0.001 |
| Bibliometrics | 0.001 | 0.002 |
| Science and technology studies | 0.001 | 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".