A retrospective, cohort study of the quality of acute myocardial infarction care in Ontario, Canada
Bibliographic record
Abstract
Introduction. Increasing demands from patients for accountability from health care providers has lead to an interest in the quality of health care for diseases posing a large, economic and health burden, such as acute myocardial infarction (AMI). However, relatively little is known about the quality of cardiac care provided in Canada. To address these issues, we developed quality indicators for measuring and improving AMI care in Canada, compared the quality of care provided to elderly and non-elderly residents of Ontario, and developed a statistical model to predict 30-day mortality after an AMI in Ontario. Methods. Trained chart abstractors collected demographic and clinical information from 44 hospitals in Ontario for 5,131 AMI patients admitted between fiscal years 1994–1996 to form the Enhanced Feedback for Effective Cardiac Treatment (EFFECT 1) database. A modified-Delphi panel approach was employed to develop a set of AMI quality indicators from a national expert panel of nine health care practitioners. These quality indicators were then tested in the EFFECT 1 database, and results between the elderly and non-elderly were compared. Logistic regression analysis using backward elimination was performed to determine clinical predictors of 30-day AMI mortality. Results. A comprehensive set of Canadian AMI quality indicators were developed including inpatient, pharmacological, process of care indicators. These indicators were then applied to the EFFECT 1 data set (N = 5,131) and demonstrated underutilization of evidence-based therapies across all age groups. The odds of ideal (i.e., no contraindications to therapy) elderly candidates receiving treatment were consistently less than that of the non-elderly with the exception of ACEIs at discharge (OR: 1.46 [95% CI: 1.22–1.74]). A simple, six variable (age, cardiac arrest, shock, pulmonary edema, systolic blood pressure, white blood cell count) logistic regression model with good discriminatory ability (c statistic = 0.81) was developed and validated to predict AMI mortality. Conclusion. The quality of AMI care can be measured using a series of AMI quality indicators and an AMI mortality prediction rule. Our results suggest that significant opportunities exist for further AMI quality improvement initiatives in Canada.
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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 | 0.001 |
| Meta-epidemiology (broad) | 0.001 | 0.001 |
| Bibliometrics | 0.002 | 0.006 |
| Science and technology studies | 0.003 | 0.001 |
| Scholarly communication | 0.001 | 0.001 |
| Open science | 0.001 | 0.001 |
| 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".