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.
Fetched live from OpenAlex and de-inverted. Abstracts are not stored in this database: the inverted indexes are 8.6 GB of the frame’s 9.3 GB of text, and the host has 13 GB free.
How this classification was reachedexpand
Full frame distilled prediction
Teacher imitationNot calibrated prevalence, not ground truth. Human validation pending. Learned from the 10,348 direct Codex labels and 10,348 direct Gemma labels. Candidate is the union of thresholded teacher heads; consensus is their intersection. These outputs are machine_predicted_unvalidated and are not human labels or direct frontier model labels.
Codex and Gemma teacher scores by category
| Category | Codex | Gemma |
|---|---|---|
| Metaresearch | 0.000 | 0.000 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.001 | 0.000 |
| Bibliometrics | 0.000 | 0.000 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.000 | 0.000 |
| Open science | 0.000 | 0.000 |
| Research integrity | 0.000 | 0.000 |
| Insufficient payload (model declined to judge) | 0.000 | 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 teacher head, 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".