Bibliographic record
Abstract
Although the direct oral anticoagulants (DOACs) do not require routine monitoring and reduce bleeding compared with warfarin, there are special circumstances in which laboratory measurement or reversal of their anticoagulant effect may be indicated. The dilute thrombin time and ecarin-based assays are able to quantify dabigatran across a broad range of concentrations, but are not widely available. A normal thrombin time excludes clinically relevant levels and a normal activated partial thromboplastin time probably excludes excess levels of dabigatran. Factor Xa inhibitors may be quantified with an anti-Xa assay calibrated with drug-specific standards. A normal prothrombin time probably excludes excess levels of rivaroxaban and edoxaban, but not apixaban. Patients with minor and moderate DOAC-associated bleeding can be treated with supportive care and general hemostatic measures. Nonspecific reversal agents (eg, prothrombin complex concentrate, activated prothrombin complex concentrate) are of unproven benefit, carry a risk of thrombosis, and should be reserved for severe bleeding. Specific reversal agents, such as idarucizumab (a monoclonal antibody fragment that binds dabigatran) and andexanet alfa (a recombinant factor Xa variant that binds factor Xa inhibitors but lacks coagulant activity), are in clinical development.
Fetched live from OpenAlex and de-inverted. Abstracts are not stored in this database: the inverted indexes are 8.6 GB of the frame’s 9.3 GB of text, and the host has 13 GB free.
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 | 0.002 |
| Meta-epidemiology (narrow) | 0.001 | 0.000 |
| Meta-epidemiology (broad) | 0.002 | 0.001 |
| Bibliometrics | 0.003 | 0.002 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.001 | 0.001 |
| Open science | 0.001 | 0.001 |
| Research integrity | 0.001 | 0.001 |
| Insufficient payload (model declined to judge) | 0.004 | 0.002 |
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".