Oral Direct Factor Xa Inhibitors
Bibliographic record
Abstract
Vitamin K antagonists, such as warfarin, have been the mainstay of oral anticoagulation for many decades. Although effective, warfarin has numerous limitations, including a variable dose requirement from patient to patient because of differences in dietary vitamin K intake, common genetic polymorphisms, and multiple drug interactions that affect its pharmacodynamics and metabolism. Consequently, warfarin requires frequent monitoring to ensure that a therapeutic anticoagulant effect has been achieved because excessive anticoagulation can lead to bleeding, and because insufficient anticoagulation can result in thrombosis. Such monitoring is burdensome for patients and physicians and is costly for the health care system. These limitations have prompted the development of new oral anticoagulants that target either factor Xa or thrombin. Although the path to the development of these drugs has been long, the new drugs are at least as effective and safe as warfarin, but they streamline clinical care because they can be administered in fixed doses without routine coagulation monitoring. This article focuses on rivaroxaban, apixaban, and edoxaban, the oral factor Xa inhibitors in the most advanced stages of development. After 20 years of discovery research, these agents are already licensed for several indications. Thus, the long path to finding replacements for warfarin has finally reached fruition. Therefore, development of the oral factor Xa inhibitors represents a translational science success story.
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 | 0.000 | 0.001 |
| Meta-epidemiology (narrow) | 0.001 | 0.000 |
| Meta-epidemiology (broad) | 0.001 | 0.001 |
| 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) | 0.008 | 0.006 |
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".