Likelihood of coronary angiography among First Nations patients with acute myocardial infarction
Bibliographic record
Abstract
BACKGROUND: Morbidity due to cardiovascular disease is high among First Nations people. The extent to which this may be related to the likelihood of coronary angiography is unclear. We examined the likelihood of coronary angiography after acute myocardial infarction (MI) among First Nations and non-First Nations patients. METHODS: Our study included adults with incident acute MI between 1997 and 2008 in Alberta. We determined the likelihood of angiography among First Nations and non-First Nations patients, adjusted for important confounders, using the Alberta Provincial Project for Outcome Assessment in Coronary Heart Disease (APPROACH) database. RESULTS: Of the 46,764 people with acute MI, 1043 (2.2%) were First Nations. First Nations patients were less likely to receive angiography within 1 day after acute MI (adjusted odds ratio [OR] 0.73, 95% confidence interval [CI] 0.62-0.87). Among First Nations and non-First Nations patients who underwent angiography (64.9%), there was no difference in the likelihood of percutaneous coronary intervention (PCI) (adjusted hazard ratio [HR] 0.92, 95% CI 0.83-1.02) or coronary artery bypass grafting (CABG) (adjusted HR 1.03, 95% CI 0.85-1.25). First Nations people had worse survival if they received medical management alone (adjusted HR 1.38, 95% CI 1.07-1.77) or if they underwent PCI (adjusted HR 1.38, 95% CI 1.06-1.80), whereas survival was similar among First Nations and non-First Nations patients who received CABG. INTERPRETATION: First Nations people were less likely to undergo angiography after acute MI and experienced worse long-term survival compared with non-First Nations people. Efforts to improve access to angiography for First Nations people may improve outcomes.
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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 | 0.000 | 0.000 |
| Scholarly communication | 0.001 | 0.000 |
| Open science | 0.001 | 0.000 |
| 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".