P3863Trauma victims requiring dabigatran reversal with idarucizumab in RE-VERSE AD
Bibliographic record
Abstract
Background: Idarucizumab is licensed for dabigatran reversal based on the results of the RE-VERSE AD study, which showed rapid and complete reversal of dabigatran anticoagulation in patients presenting with severe bleeding (Group A), or in those requiring urgent surgery (Group B). Enrollment was based solely on the clinical decision to reverse anticoagulation. This analysis focuses on the outcomes in the cohort of trauma patients. Methods: Trauma patients on dabigatran could be enrolled in either group in RE-VERSE-AD, whether for serious bleeding or for reversal prior to urgent surgery. All patients were given 5 grams of idarucizumab intravenously and the primary endpoint was maximum reversal of dabigatran anticoagulation in the first 4 hours, as measured by ecarin clot time or diluted thrombin time. Results: Of the 503 patients enrolled in RE-VERSE AD, there were 114 trauma victims, 80 enrolled in Group A and 34 in Group B (Table). The most commonly documented mechanism of injury was fall from standing height, resulting in open or closed head injury or pelvic or hip fractures; 6 patients sustained high-impact polytrauma. Other injuries included fractured ribs, broken nose, and severed fingers. No trauma patient received more than one dose of idarucizumab, all patients had 100% reversal, and no drug-related adverse events were reported. Thrombotic events rates were low and consistent with the entire study cohort.
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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 | 0.001 |
| 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 | 0.000 | 0.001 |
| Insufficient payload (model declined to judge) | 0.004 | 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".