Management of major bleeding events in patients treated with rivaroxaban vs. warfarin: results from the ROCKET AF trial
Bibliographic record
Abstract
AIMS: There are no data regarding management and outcomes of major bleeding events in patients treated with oral factor Xa inhibitors. METHODS AND RESULTS: Using data from ROCKET AF, we analysed the management and outcomes of major bleeding overall and according to the randomized treatment. During a median follow-up of 1.9 years, 779 (5.5%) patients experienced major bleeding at a rate of 3.52 events/100 patient-years with a similar event rate in each arm (n = 395 rivaroxaban vs. n = 384 warfarin). The median number of transfused packed red blood cells (PRBC) per episode was similar in both arms [2 (25th, 75th: 2, 4) units]. Overall, few transfusions of whole blood (n = 14), platelets (n = 10), or cryoprecipitate (n = 2) were used. Transfusion of fresh frozen plasma (FFP) was significantly less in the rivaroxaban arm (n = 45 vs. n = 81 units) after adjustment for covariates [odds ratio (OR) 0.43 (95% CI 0.29-0.66); P < 0.0001]. Prothrombin complex concentrates (PCC) were administered less in the rivaroxaban arm (n = 4 vs. n = 9). Outcomes after major bleeding, including stroke or non-central nervous system embolism (4.7% rivaroxaban vs. 5.4% warfarin; HR 0.89; 95% CI 0.42-1.88) and all-cause death (20.4% rivaroxaban vs. 26.1% warfarin; HR 0.69, 95% CI 0.46-1.04) were similar in patients treated with rivaroxaban and warfarin (interaction P = 0.51 and 0.11). CONCLUSION: Among high-risk patients with atrial fibrillation who experienced major bleeding in ROCKET AF, the use of FFP and PCC was less among those allocated rivaroxaban compared with warfarin. However, use of PRBCs and outcomes after bleeding were similar among patients randomized to rivaroxaban or to warfarin.
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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 | 0.004 | 0.005 |
| Meta-epidemiology (narrow) | 0.001 | 0.000 |
| Meta-epidemiology (broad) | 0.002 | 0.003 |
| 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 | 0.001 | 0.002 |
| Insufficient payload (model declined to judge) | 0.003 | 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".