Bibliographic record
Abstract
Despite advances in anticoagulant therapy, thrombosis remains the leading cause of morbidity and mortality worldwide. Heparin and vitamin K antagonists (VKAs), the first anticoagulants to be used successfully for the prevention and treatment of thrombosis, are associated with a risk of bleeding. These agents target multiple coagulation factors. Thus, by activating antithrombin, heparin mainly inhibits factor Xa and thrombin, whereas VKAs lower the levels of the vitamin K-dependent clotting factors. Direct oral anticoagulants, which have replaced VKAs for many indications, inhibit only factor Xa or thrombin. Although the direct oral anticoagulants are associated with less bleeding than VKAs, bleeding remains their major side effect. Epidemiological and animal studies have identified factor XI as a target for potentially safer anticoagulant drugs because factor XI deficiency or inhibition protects against thrombosis and is associated with little or no bleeding. Several factor XI-directed strategies are currently under investigation. This article (1) reviews the rationale for the development of factor XI inhibitors, (2) identifies the agents in most advanced stages of development, (3) describes the results of completed clinical trials and provides a summary of those underway, and (4) highlights the opportunities and challenges for this next generation of anticoagulants.
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 | 0.001 |
| Meta-epidemiology (narrow) | 0.001 | 0.000 |
| Meta-epidemiology (broad) | 0.001 | 0.000 |
| Bibliometrics | 0.002 | 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 | 0.002 |
| 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".