Meta-analysis Comparing Outcomes of Type 2 Myocardial Infarction and Type 1 Myocardial Infarction With a Focus on Dual Antiplatelet Therapy
Bibliographic record
Abstract
Background There are important knowledge gaps in type 2 myocardial infarction (T2MI). Our primary objective was to compare the outcomes of patients with T2MI with those of patients with type 1 myocardial infarction (T1MI). Our secondary objective was to determine whether randomized controlled trials (RCTs) evaluating dual antiplatelets (DAPTs) have explicitly included patients with T2MI. Methods We performed a meta-analysis comparing outcomes of patients with T2MI with patients with T1MI and a separate systematic review to evaluate the inclusion of T2MI in RCTs evaluating DAPT. There were 19 cohorts enrolling 48,829 patients (40,604 with T1MI and 5361 with T2MI) and 51 RCTs enrolling 188,132 patients with acute coronary syndrome. Results Patients with T2MI had approximately 2-fold increases in unadjusted odds of long-term mortality compared with patients with T1MI (odds ratio, 2.47; 95% confidence interval, 2.06-2.96; P < 0.0001) and a 45% increase in adjusted odds of long-term mortality (odds ratio, 1.45; 95% confidence interval, 1.25-1.69; P < 0.0001, respectively). There was no published evaluation of efficacy, effectiveness, and safety of DAPT in patients with T2MI. Conclusion Patients with T2MI are at increased risk of adjusted all-cause long-term mortality compared with patients with T1MI. The role of DAPT remains unclear in T2MI.
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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 | 0.020 | 0.043 |
| Meta-epidemiology (narrow) | 0.003 | 0.001 |
| Meta-epidemiology (broad) | 0.012 | 0.058 |
| Bibliometrics | 0.005 | 0.005 |
| Science and technology studies | 0.001 | 0.001 |
| Scholarly communication | 0.004 | 0.001 |
| Open science | 0.002 | 0.001 |
| Research integrity | 0.002 | 0.002 |
| Insufficient payload (model declined to judge) | 0.003 | 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".