Bibliographic record
Abstract
For more than 50 years, heparin(s) and warfarin have been the most important anticoagulant agents, and clinicians are accustomed to their specific antidotes (protamine sulfate and vitamin K/plasma [or factor concentrates], respectively). Recently, there has been an explosion of novel anticoagulant development: ideally, these newer agents should have advantages over traditional anticoagulants, such as fewer side effects, a more predictable pharmacokinetic profile (and potentially no need for monitoring), minimal drug-drug interactions, and so forth. But, unlike the older agents, the newer anticoagulants do not have specific antidotes. There is increasing focus on the use of nonspecific procoagulants, such as non-activated and activated prothrombin complex concentrates (PCCs) and recombinant factor VIIa (rFVIIa), to manage major bleeding or need for emergency invasive procedures. This paper reviews several of the novel anticoagulants and presents the available evidence for their "reversal". Based on extrapolation from animal models, clinical anecdote, and an understanding of their mechanism of action, we recommend treating major bleeding complications of DTIs, as follows (in descending order of preference): activated PCCs; rFVIIa; and (non-activated) PCCs. For management of fondaparinux-associated bleeding, rFVIIa has some rationale (for which we provide an illustrative case). The increasing use of novel anticoagulants will require physicians to have an understanding of rational approaches to "reverse" their anticoagulant effects when true antidotes do not exist.
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 | 0.002 | 0.006 |
| Meta-epidemiology (narrow) | 0.001 | 0.000 |
| Meta-epidemiology (broad) | 0.001 | 0.001 |
| Bibliometrics | 0.001 | 0.000 |
| Science and technology studies | 0.001 | 0.001 |
| Scholarly communication | 0.002 | 0.002 |
| Open science | 0.001 | 0.001 |
| Research integrity | 0.002 | 0.004 |
| Insufficient payload (model declined to judge) | 0.014 | 0.007 |
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".