Transferring from clopidogrel loading dose to prasugrel loading dose in acute coronary syndrome patients
Bibliographic record
Abstract
High on-treatment platelet reactivity (HPR) has been identified as an independent risk factor for ischaemic events. The randomised, double-blind, TRIPLET trial included a pre-defined comparison of HPR in acute coronary syndrome (ACS) patients undergoing percutaneous coronary intervention (PCI) following a placebo/600-mg clopidogrel loading dose (LD) immediately before a subsequent prasugrel 60-mg or 30-mg LD. Platelet reactivity was assessed using the VerifyNow® P2Y12 assay (P2Y12 Reaction Units, PRU) within 24 hours (h) following the placebo/clopidogrel LD (immediately prior to prasugrel LD), and at 2, 6, 24, 72 h following prasugrel LDs. The impact of CYP2C19 predicted metaboliser phenotype (extensive metabolisers [EM] and reduced metabolisers [RM]) on HPR status was also assessed. HPR (PRU ≥240) following the clopidogrel LD (prior to the prasugrel LD) was 58.5% in the combined clopidogrel LD groups. No significant difference was noted when stratified by time between the clopidogrel and prasugrel LDs (≤6 hs vs>6 h). At 6 h following the 2nd loading dose in the combined prasugrel LD groups, HPR was 7.1%, with 0% HPR by 72 h. There was no significant effect of CYP2C19 genotype on pharmacodynamic (PD) response following either prasugrel LD treatments at any time point, regardless of whether it was preceded by a clopidogrel 600-mg LD. In conclusion, in this study, patients with ACS intended for PCI showed a high prevalence of HPR after clopidogrel 600-mg LD regardless of metaboliser status. When prasugrel LD was added, HPR decreased substantially by 6 h, and was not seen by 72 h.
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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) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.001 | 0.002 |
| Bibliometrics | 0.000 | 0.000 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.001 | 0.000 |
| Open science | 0.000 | 0.000 |
| Research integrity | 0.001 | 0.002 |
| Insufficient payload (model declined to judge) | 0.002 | 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".