Specific antidotes for bleeding associated with direct oral anticoagulants
Bibliographic record
Abstract
#### What you need to know An 83 year old woman is brought to the emergency department by her care giver who noticed her altered mental status following two episodes of passing black faeces. The woman has a history of ischaemic stroke associated with atrial fibrillation and hypertension and is on dabigatran for stroke prevention. She took her last dose a few hours ago. Computed tomography of the head shows no acute intracranial abnormalities. Abdominal and rectal exams are normal. Her blood pressure is 82/56 mm Hg. The multidisciplinary team in charge of her care arrives to talk with her and her family about antidotes for reversing her anticoagulation. Direct oral anticoagulants (DOACs) are a relatively new class of oral anticoagulants developed as alternatives to vitamin K antagonists such as warfarin, and are indicated in non-valvular atrial fibrillation and venous thromboembolism (Box 1). #### Box 1: Direct oral anticoagulants (DOACs) ##### Commercially available DOACs include Randomised controlled trials and meta-analyses among patients with non-valvular atrial fibrillation or acute venous thromboembolism have shown DOACs to be at least as effective as vitamin K antagonists in preventing or treating thromboembolic events.3 4 …
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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 | 0.000 | 0.003 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.001 |
| Bibliometrics | 0.000 | 0.000 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.000 | 0.001 |
| Open science | 0.000 | 0.000 |
| Research integrity | 0.001 | 0.001 |
| Insufficient payload (model declined to judge) | 0.013 | 0.003 |
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".