Secondary prevention after acute myocardial infarction in four Canadian provinces, 1997-2000.
Bibliographic record
Abstract
BACKGROUND: Publication of population-based analyses of medication use after acute myocardial infarction (AMI) could encourage the use of effective secondary prevention medications. OBJECTIVE: To describe outpatient use of beta-blockers, angiotensin-converting enzyme (ACE) inhibitors, statins, calcium channel blockers and nitrates in elderly survivors of AMI over the fiscal years from 1997/98 to 1999/2000 in Nova Scotia, Quebec, Ontario and British Columbia. METHODS: Linked administrative databases were used to identify all AMI patients 65 years of age or older admitted in Quebec (n=14,880), Ontario (n=28,647) and British Columbia (n=7549) over the study period, and to measure 90-day postdischarge utilization rates of cardiac medications for these patients. A population-based clinical registry was used to measure rates of prescription at discharge for elderly patients in Nova Scotia admitted to an acute care hospital from 1997 to 2000 (n=1997). RESULTS: Utilization rates for beta-blockers, ACE inhibitors and statins increased over time, while rates for calcium channel blockers and nitrates decreased only slightly. The largest increases were for statins (Nova Scotia: 26% to 42%, Quebec: 27% to 43%; Ontario: 28% to 40%; British Columbia: 30% to 42%) and for ACE inhibitors in Ontario (55% to 65%) and Nova Scotia (46% to 68%). Of the three drugs recommended for secondary prevention, overall utilization rates for beta-blockers were highest in Nova Scotia, lowest in British Columbia, and similar in Quebec and Ontario. Rates for ACE inhibitors were highest in Ontario and similar in Quebec, Nova Scotia and British Columbia. Rates for statins were slightly higher in Quebec and British Columbia than in Ontario and Nova Scotia. The proportion of patients without a prescription for any of the recommended drugs was highest in British Columbia (20%), lowest in Nova Scotia (8%), and similar in Quebec and Ontario (Ontario: 12%; Quebec: 13%). There was marked regional variation in utilization rates within the four provinces. CONCLUSIONS: Although utilization rates for recommended cardiac medications are increasing over time, there remains room for improvement. Overall utilization rates and temporal trends are generally similar in all four provinces, but there are wide regional variations within provinces.
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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) | 0.001 | 0.000 |
| Meta-epidemiology (broad) | 0.001 | 0.001 |
| Bibliometrics | 0.002 | 0.006 |
| Science and technology studies | 0.002 | 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".