Direct thrombin inhibitors in acute coronary syndromes: effect in patients undergoing early percutaneous coronary intervention
Bibliographic record
Abstract
AIMS: We evaluated the effect of direct thrombin inhibitors (DTIs) in patients undergoing early percutaneous coronary intervention (PCI), using the DTI Trialists' Collaboration database of 35,970 patients from 11 randomized trials of DTIs vs. heparin. METHODS AND RESULTS: We performed a Cox proportional hazards regression analysis with PCI as a time-dependent covariate to assess the independent impact of DTIs according to the performance of early PCI. PCI was performed in 7049 patients in the first 72 h after randomization. In trials in which PCI was not planned, DTIs were associated with a 10% relative risk reduction in death or myocardial infarction at 30 days (HR=0.90, 95% CI: 0.84-0.97). This benefit was found to be greater in patients undergoing early PCI (HR=0.66, 95% CI: 0.48-0.91) than those undergoing non-early PCI (HR=0.94, 95% CI: 0.86-1.03). After adjustment for baseline characteristics and propensity to undergo PCI, the risk of death or myocardial infarction remained lower with DTI (HR=0.62, 95% CI: 0.44-0.89). CONCLUSION: After adjustment for baseline differences and propensity to undergo early PCI, DTIs appeared to be more effective than heparin in reducing death or re-infarction among patients undergoing early PCI.
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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 | 0.004 | 0.012 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.001 | 0.002 |
| Bibliometrics | 0.000 | 0.001 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.001 | 0.000 |
| Open science | 0.000 | 0.000 |
| Research integrity | 0.001 | 0.001 |
| 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".