Determinants of high platelet reactivity in patients with acute coronary syndromes treated with ticagrelor
Bibliographic record
Abstract
High platelet reactivity (HPR) is a risk factor for stent thrombosis, a potentially lethal complication of percutaneous coronary intervention. HPR is also associated with increased risk of myocardial infarction and death in invasively-treated patients with acute coronary syndrome (ACS). HPR occurs even in ACS patients treated with ticagrelor, a state-of-the-art antiplatelet agent, especially during the first hours of treatment. Patient-level pharmacodynamic data obtained from 102 ACS subjects enrolled in two prospective, pharmacodynamic trials were analysed in order to identify clinical features related with increased odds of on-ticagrelor HPR during the first two hours after ticagrelor loading dose in ACS patients. Presence of ST-segment elevation myocardial infarction (versus non-ST-segment elevation ACS) and morphine co-administration were the strongest predictors of HPR at 1 and 2 hours after ticagrelor loading dose according to linear regression analyses, multiple backward stepwise logistic regression analyses and generalized estimating equation model. By pinpointing simple to recognize clinical features, the results of this study facilitate identification of ACS patients who have the highest odds of HPR during the initial phase of treatment with ticagrelor, and who could potentially benefit from alternative treatment strategies.
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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 | 0.003 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| 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 | 0.000 | 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".