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 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 | 0.002 | 0.015 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.001 | 0.001 |
| Bibliometrics | 0.001 | 0.001 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.001 | 0.000 |
| Open science | 0.000 | 0.000 |
| Research integrity | 0.001 | 0.001 |
| 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".