Management and Outcomes of Major Bleeding During Treatment With Dabigatran or Warfarin
Bibliographic record
Abstract
BACKGROUND: The aim of this study was to compare the management and prognosis of major bleeding in patients treated with dabigatran or warfarin. METHODS AND RESULTS: Two independent investigators reviewed bleeding reports from 1034 individuals with 1121 major bleeds enrolled in 5 phase III trials comparing dabigatran with warfarin in 27 419 patients treated for 6 to 36 months. Patients with major bleeds on dabigatran (n=627 of 16 755) were older, had lower creatinine clearance, and more frequently used aspirin or non-steroid anti-inflammatory agents than those on warfarin (n=407 of 10 002). The 30-day mortality after the first major bleed tended to be lower in the dabigatran group (9.1%) than in the warfarin group (13.0%; pooled odds ratio, 0.68; 95% confidence interval, 0.46-1.01; P=0.057). After adjustment for sex, age, weight, renal function, and concomitant antithrombotic therapy, the pooled odds ratio for 30-day mortality with dabigatran versus warfarin was 0.66 (95% confidence interval, 0.44-1.00; P=0.051). Major bleeds in dabigatran patients were more frequently treated with blood transfusions (423/696, 61%) than bleeds in warfarin patients (175/425, 42%; P<0.001) but less frequently with plasma (dabigatran, 19.8%; warfarin, 30.2%; P<0.001). Patients who experienced a bleed had shorter stays in the intensive care unit if they had previously received dabigatran (mean 1.6 nights) compared with those who had received warfarin (mean 2.7 nights; P=0.01). CONCLUSIONS: Patients who experienced major bleeding on dabigatran required more red cell transfusions but received less plasma, required a shorter stay in intensive care, and had a trend to lower mortality compared with those who had major bleeding on warfarin. CLINICAL TRIAL REGISTRATION URL: http://www.ClinicalTrials.gov. Unique identifiers: NCT00262600, NCT00291330, NCT00680186, NCT00329238 and NCT00558259.
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How this classification was reachedexpand
Full frame distilled prediction
Teacher imitationNot calibrated prevalence, not ground truth. Human validation pending. Learned from the 10,348 direct Codex labels and 10,348 direct Gemma labels. Candidate is the union of thresholded teacher heads; consensus is their intersection. These outputs are machine_predicted_unvalidated and are not human labels or direct frontier model labels.
Codex and Gemma teacher scores by category
| Category | Codex | Gemma |
|---|---|---|
| Metaresearch | 0.000 | 0.000 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.001 | 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) | 0.000 | 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 teacher head, 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".