Reversal of the antiplatelet effect of ticagrelor by simulated platelet transfusion
Bibliographic record
Abstract
BACKGROUND Reversal of antiplatelet therapy is desirable in patients presenting with life‐threatening bleeding or requiring urgent surgery. This study aimed to examine ticagrelor reversal using donor platelets and to explore the effects of residual ticagrelor on donor platelets. STUDY DESIGN AND METHODS In Cohort 1, 16 healthy subjects were treated with ticagrelor 90 mg twice daily alone or in combination with aspirin 100 mg once daily for 7 days followed by single blood sampling for preparation of platelet‐rich plasma. An additional 16 healthy subjects served as controls. In Cohort 2, 16 healthy subjects were treated with ticagrelor 90 mg twice daily or clopidogrel 75 mg once daily for 7 days followed by serial blood samplings for preparation of platelet‐poor plasma (PPP). An additional 16 healthy subjects served as controls. RESULTS In Cohort 1, inhibition of adenosine diphosphate–induced platelet aggregation (PLADP) by ticagrelor could not be fully reversed by mixing with up to 90% control platelets, whereas inhibition of arachidonic acid–induced platelet aggregation by aspirin was fully reversed with the addition of 60% control platelets. In Cohort 2, 10% PPP obtained from ticagrelor‐treated subjects reduced PLADP from 74% to 40% at 2 hours, 72% to 58% at 6 hours, and 73% to 59% at 10 hours, while 10% or 20% PPP obtained from clopidogrel‐treated subjects did not inhibit PLADP. CONCLUSION The antiplatelet effect of ticagrelor cannot be fully reversed by donor platelets, which could be explained by the presence of active drug. The effect of residual drug on donor platelets appears to be evident for at least 10 hours after ticagrelor ingestion.
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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 | 0.000 | 0.001 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.000 | 0.000 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.000 | 0.000 |
| Open science | 0.000 | 0.000 |
| Research integrity | 0.000 | 0.000 |
| 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".