Ximelagatran: the first oral direct thrombin inhibitor
Bibliographic record
Abstract
Oral anticoagulants are often prescribed for long-term prevention and treatment of venous or arterial thromboembolism. The only orally active anticoagulants currently available are the vitamin K antagonists. Although effective, they have a narrow therapeutic window and require routine coagulation monitoring to ensure that a therapeutic level has been achieved. Furthermore, genetic differences in metabolism and multiple food and drug interactions affect the anticoagulant response to vitamin K antagonists. These factors add to the need for routine coagulation monitoring, which is problematic for patients and physicians and costly for the healthcare system. Ximelagatran, the first oral direct thrombin inhibitor, was designed to overcome many of the drawbacks of vitamin K antagonists. Since it produces a predictable anticoagulant response, ximelagatran does not require coagulation monitoring. Phase III clinical trials have evaluated the efficacy and safety of ximelagatran for the prevention and treatment of venous thromboembolism and for the prevention of thromboembolic events in patients with atrial fibrillation. Focusing on ximelagatran, this review will discuss the appropriateness of thrombin as a target for new anticoagulants, compare and contrast direct and indirect thrombin inhibitors and describe the theoretical advantages of direct thrombin inhibitors. It will also review the pharmacology of ximelagatran, discuss the clinical trial results with ximelagatran and provide perspective on the advantages and potential limitations of ximelagatran.
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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 | 0.000 |
| 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 | 0.000 |
| Research integrity | 0.001 | 0.001 |
| Insufficient payload (model declined to judge) | 0.007 | 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".