Differential effects of dual antiplatelet and dual antithrombotic therapy on hemostasis in chronic coronary syndrome patients: the DEFINE CCS study
Bibliographic record
Abstract
Optimal long term antithrombotic treatment in high-risk chronic coronary syndrome (CCS) patients remains uncertain. Both ticagrelor (60 mg BID) and low-dose rivaroxaban (2.5 mg BID) in addition to low-dose aspirin resulted in significant reductions in major cardiovascular events in high-risk patients at the expense of increased bleeding risk. We aimed to compare the effects of both strategies on bleeding time, fibrin clot lysis time and inflammatory biomarkers in CCS patients with history of acute coronary syndrome. Twenty aspirin-treated patients were recruited into a randomized crossover study to receive ticagrelor 60 mg BID in 1 week and rivaroxaban 2.5 mg BID in the other with a 2-week washout period in between. Outcome measures were determined at the start and end of each treatment week. Two-way ANOVA was used to determine difference in treatment effect. Data are presented as mean ± SD. At baseline, there was no significant difference in any studied outcome measure. Bleeding time was significantly longer with ticagrelor compared to rivaroxaban (Ticagrelor: 897 ± 481secs vs. Rivaroxaban: 440 ± 184 secs; p = .0001). Fibrin clot lysis time was not impacted by ticagrelor but significantly dropped post treatment with rivaroxaban (Ticagrelor: 5743 ± 2590 secs vs. Rivaroxaban: 4309 ± 2308 secs; p = .0049). Neither treatment had an impact on levels of high-sensitivity CRP or white cell count. In conclusion, ticagrelor 60 mg BID has greater impact on bleeding time compared to rivaroxaban 2.5 mg BID. Whereas rivaroxaban, positively modulates fibrin clots, rendering them more prone to lysis.
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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 | 0.003 | 0.002 |
| Meta-epidemiology (narrow) | 0.001 | 0.001 |
| Meta-epidemiology (broad) | 0.003 | 0.002 |
| Bibliometrics | 0.000 | 0.000 |
| Science and technology studies | 0.000 | 0.001 |
| Scholarly communication | 0.001 | 0.001 |
| Open science | 0.001 | 0.001 |
| Research integrity | 0.001 | 0.001 |
| 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".