Processes and Outcomes of Care for Diabetic Acute Myocardial Infarction Patients in Ontario
Bibliographic record
Abstract
OBJECTIVE: To compare the health service utilization and long-term outcomes of acute myocardial infarction (AMI) patients with and without diabetes in Ontario. RESEARCH DESIGN AND METHODS: We examined 25,697 patients from Ontario (6,052 and 19,645 patients with and without diabetes, respectively) who were hospitalized because of AMI between 1 April 1992 and 31 December 1993. Using linked administrative databases, we determined the use of invasive cardiac procedures at 1 year as well as the intensity of specialty follow-up care and use of evidence-based pharmacotherapies (among elderly individuals) within the first 90 days of hospital discharge. Outcomes examined included mortality and recurrent cardiac admissions at 30 days and 5 years post AMI. Multivariable analyses adjusted for sociodemographic and case-mix characteristics, attending physician specialty, and admitting hospital characteristics. RESULTS: Despite being at significantly higher risk for death at baseline, diabetic patients were less likely to be followed-up by a cardiologist (22.2 vs. 25.6%, P < 0.001), to receive myocardial revascularization (12.6 vs. 14.9%, P < 0.001), to receive beta-blockers (34.2 vs. 44.0%, P < 0.001), and to receive aspirin therapy (59.7 vs. 63.5%, P < 0.001) after AMI than their nondiabetic counterparts. Diabetes was an important independent predictor of 5-year morbidity (adjusted hazard ratio 1.52, 95% CI 1.45-1.59) and 5-year mortality outcomes (1.57, 1.50-1.63). Variations in processes of care were marginally associated with higher nonfatal complication rates for diabetic patients. CONCLUSIONS: When managing AMI patients with diabetes in Ontario, physician treatment aggressiveness does not correspond appropriately to the baseline risk of patients.
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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 | 0.000 | 0.003 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.000 | 0.001 |
| Science and technology studies | 0.001 | 0.000 |
| Scholarly communication | 0.001 | 0.000 |
| Open science | 0.001 | 0.001 |
| Research integrity | 0.000 | 0.000 |
| 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".