Ticagrelor versus aspirin and vein graft patency after coronary bypass: A randomized trial
Bibliographic record
Abstract
BACKGROUND: Antiplatelet therapy prevents saphenous vein graft (SVG) occlusion and improves outcomes after coronary artery bypass graft surgery (CABG). However, the optimal postoperative antiplatelet regimen remains unclear. The goal of the Ticagrelor Antiplatelet Therapy to Reduce Graft Events and Thrombosis (TARGET) trial was to assess whether early postoperative ticagrelor reduces SVG occlusion compared to conventional aspirin therapy. METHODS: In this multi-center double-blind randomized trial, 250 patients who had CABG with SVG were randomized to receive either aspirin 81 mg twice daily or ticagrelor 90 mg twice daily. The primary outcome was SVG occlusion at 1 year. RESULTS: Altogether, 123 patients were randomized to aspirin and 127 received ticagrelor. One-year graft assessment was performed in 202 patients (80.8%), examining 588 grafts, yielding an overall graft occlusion rate of 10.9%. The primary outcome, SVG occlusion at 1 year, did not significantly differ between the two groups (17.4% vs. 13.2%, aspirin vs. ticagrelor, p = .30). The incidence of vein grafts with any disease (stenosis or occlusion) did not significantly differ between the groups (21.5% vs. 22.3%, aspirin vs. ticagrelor, p = .90), and the number of patients with vein graft disease did not significantly differ between the groups (29.4% vs. 28.0%, aspirin vs. ticagrelor, p = .88). Freedom from major adverse cardiovascular events at 1 year was similar between the groups (p = .60). CONCLUSIONS: Compared to conventional aspirin therapy, ticagrelor did not significantly reduce vein graft occlusion 1 year after CABG. Further study will assess the impact of ticagrelor on 2-year graft patency for this cohort.
Fetched live from OpenAlex and de-inverted. Abstracts are not stored in this database: the inverted indexes are 8.6 GB of the frame’s 9.3 GB of text, and the host has 13 GB free.
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 | 0.004 |
| Meta-epidemiology (narrow) | 0.001 | 0.001 |
| Meta-epidemiology (broad) | 0.003 | 0.002 |
| Bibliometrics | 0.000 | 0.001 |
| Science and technology studies | 0.000 | 0.001 |
| Scholarly communication | 0.001 | 0.001 |
| Open science | 0.001 | 0.000 |
| Research integrity | 0.002 | 0.002 |
| Insufficient payload (model declined to judge) | 0.008 | 0.001 |
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".