Investigating the determinants of decreasing postmyocardial infarction mortality: an analysis of in-hospital data from 1992 and 1997.
Bibliographic record
Abstract
BACKGROUND: Although acute myocardial infarction (AMI) is the leading cause of mortality in the industrialized world, postmyocardial infarction mortality rates have been declining in recent decades. Two possible contributing factors toward this encouraging trend include changing patient characteristics and improved patient management. OBJECTIVES: To compare temporal changes in the characteristics and management of patients with AMI at a tertiary care hospital (Sunnybrook and Women's College Health Sciences Centre) in Toronto, Ontario. METHODS: Two hundred hospital charts of patients with AMI as the most responsible diagnosis were reviewed (100 from 1992 and 100 from 1997). One hundred thirty prespecified variables were extracted from each chart, with emphasis placed on baseline clinical characteristics, AMI management and survival. RESULTS: Between 1992 and 1997, AMI in-hospital mortality declined from 20% to 15%. Most baseline clinical characteristics (age, sex, comorbidity, cardiac history, and presenting symptoms and signs) were similar across the 1992 and 1997 patient populations. The only significant risk factor change involved an increase in the prevalence of hypercholesterolemia. In contrast, between 1992 and 1997 there was an increased in-hospital use of anticoagulants, antiplatelets, thrombolytics, beta-blockers, angiotensin-converting enzyme inhibitors and statins. Similarly, there was an increased use of coronary angioplasty and coronary bypass surgery. There was no significant change in the use of AMI therapies that are potentially harmful, including antiarrythmic agents and calcium channel blockers. CONCLUSIONS: AMI patient characteristics were similar between 1992 and 1997 but there were striking changes in AMI treatment patterns. The increased use of evidence-based pharmacotherapy may be the most significant contributing factor to declining postmyocardial infarction mortality.
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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 | 0.004 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.001 | 0.003 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.001 | 0.000 |
| Open science | 0.001 | 0.000 |
| 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".