Bibliographic record
Abstract
Anticoagulants are widely used for the prevention and treatment of venous and arterial thrombosis. Current treatment strategies often employ a combination of parenteral and oral agents because the only available orally active anticoagulants, vitamin K antagonists, have a delayed onset of action. Furthermore, vitamin K antagonists have a narrow therapeutic window that necessitates careful anticoagulation monitoring, and dosing is problematic because of multiple food and drug interactions. These limitations highlight the need for oral anticoagulants that produce a more predictable anticoagulant response than vitamin K antagonists, thereby obviating the need for laboratory monitoring. Ximelagatran has the potential to meet this need. A prodrug of melagatran, an agent that targets thrombin, ximelagatran exhibits many of the characteristics of an ideal anticoagulant. This article (1). reviews the limitations of vitamin K antagonists, (2). lists the characteristics of an ideal anticoagulant, (3). rationalizes thrombin as a target for new anticoagulants, (4). reviews the preclinical and clinical data with ximelagatran, and (5). provides clinical perspective as to the future of ximelagatran and other orally active anticoagulants currently under 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 | 0.000 | 0.001 |
| Meta-epidemiology (narrow) | 0.001 | 0.000 |
| Meta-epidemiology (broad) | 0.001 | 0.000 |
| Bibliometrics | 0.001 | 0.001 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.001 | 0.001 |
| Open science | 0.001 | 0.000 |
| Research integrity | 0.001 | 0.001 |
| Insufficient payload (model declined to judge) | 0.010 | 0.008 |
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".