Consistency of benefit from an early invasive strategy after fibrinolysis: a patient-level meta-analysis
Bibliographic record
Abstract
BACKGROUND: Randomised controlled trials have demonstrated improved outcomes with an early invasive strategy compared with routine care after fibrinolysis among patients with ST-elevation myocardial infarction. However, it remains uncertain whether specific patient subsets derive differential benefit from an early invasive strategy. METHODS: Using patient-level data from seven randomised trials, we studied the relationship between treatment assignment (early invasive vs standard care) and adverse cardiovascular events. The outcomes assessed were death/reinfarction at 30 days and at 1 year, as well as death/reinfarction/recurrent ischaemia, major bleeding and stroke at 30 days. The analyses were conducted in strata (age, sex, diabetes, prior infarction, Killip class, anterior infarction and time from symptom onset to fibrinolysis) to assess for an interaction between the stratifying variable and treatment assigned. RESULTS: There were 101 deaths and 115 recurrent infarctions at 30 days in 3010 patients. There were no strata where an invasive strategy conferred a differential treatment effect. With the exception of a marginally significant interaction between Killip class and treatment for death/reinfarction at 30 days and 1 year (p values for interaction 0.044 and 0.038, respectively), no interactions between the stratifying variables and treatment assignment were observed. CONCLUSIONS: Benefit from an early invasive strategy after fibrinolysis for ST-elevation myocardial infarction is similar across patient subgroups stratified by these clinical characteristics. Therefore, prediction of risk and benefit from an early invasive strategy after fibrinolysis for ST-elevation myocardial infarction is best achieved by global risk evaluation rather than specific patient characteristics.
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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 | 0.050 | 0.073 |
| Meta-epidemiology (narrow) | 0.003 | 0.002 |
| Meta-epidemiology (broad) | 0.019 | 0.065 |
| Bibliometrics | 0.003 | 0.004 |
| Science and technology studies | 0.001 | 0.001 |
| Scholarly communication | 0.005 | 0.002 |
| Open science | 0.003 | 0.002 |
| Research integrity | 0.003 | 0.003 |
| Insufficient payload (model declined to judge) | 0.004 | 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".