Coronary Stenting in High Bleeding Risk Patients With Small Coronary Arteries Followed by One-Month Dual Antiplatelet Therapy: Onyx ONE Clear
Bibliographic record
Abstract
Background Small reference vessel diameters (RVDs) are a predictor of ischemic events after coronary stenting. Among patients at high bleeding risk (HBR) precluding long-term dual antiplatelet therapy (DAPT), those with small vessel disease (SVD) constitute an especially high-risk subgroup. Here, we evaluated the results of a durable-polymer, coronary zotarolimus-eluting stent (ZES) for the treatment of patients with SVD at HBR with 1-month DAPT. Methods In the prospective, multicenter Onyx ONE (One-Month DAPT) Clear study, 1506 patients at HBR treated with a ZES that discontinued DAPT at 30 days were included. The clinical outcomes of patients undergoing treatment of lesions with an RVD of ≤2.5 mm (SVD group, as determined by the angiographic core laboratory) were compared with patients without SVD. The primary end point was the composite of cardiac death or myocardial infarction between 1 and 12 months. Results Small vessel diameter treatment was performed in 489 (32.5%) patients. Patients with SVD were more likely to be women, have undergone a previous percutaneous intervention, and have multivessel coronary artery disease than patients without SVD. There were no significant differences in lesion, device, or procedural success between the groups. The Kaplan-Meier rate estimate of the primary end point was 8.5% and 6.8% in patients with SVD and those without SVD, respectively ( P = .425). No significant differences were found in any secondary end point. The Kaplan-Meier rate of stent thrombosis was 0.6% and 0.8% in patients with SVD and those without SVD, respectively ( P = .50). Conclusions Among patients at HBR treated with a ZES and 1-month DAPT, those with SVD had favorable 12-month ischemic and bleeding outcomes, which were comparable with those of patients with larger caliber vessels.
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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 | 0.001 |
| 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 | 0.000 | 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".