Bibliographic record
Abstract
Thromboembolism is a common cause of death and disability. Heparin or warfarin, the current standard management for thromboembolism may cause serious bleeding complications. Thrombin is the key enzyme of coagulation. Hirudin, the most potent natural thrombin-specific inhibitor, was first isolated from leech salivary fluid. Synthetic thrombin-specific inhibitors are rationally designed based on the knowledge on the structures of the activate site of thrombin. Thrombin-specific inhibitors are the current best choice for the treatment of heparin-induced thrombocytopenia (HIT). Recombinant hirudins (such as desirudin) were also approved for the prevention of thrombosis after hip or knee surgery. Bivalirudin (hirulog-1 or Angiomax), in adjunct to aspirin, was approved for prevention of thrombosis in patients with unstable angina following angioplasty. Argatroban has been used for the treatment of HIT, peripheral and cerebral thrombotic diseases. The benefit of using thrombin-specific inhibitors alone in acute myocardial infarction or unstable angina remains uncertain. Some of thrombin-specific inhibitors which are small molecules are orally active. The major concern for the use of thrombin-specific inhibitors is bleeding complication. The efficacy, safety, stability and oral bioavailability may be considerably improved through structural optimization. A growing line of evidence suggests that statins, the most commonly prescribed cholesterol lowering drug, may inhibit thrombin generation. Statins do not cause bleeding and have an outstanding safety profile. The findings suggest that further development of thrombin-specific inhibitors and exploration of the potential applications of non-specific thrombin inhibitors, including statins, may improve the prevention and management of thromboembotic events.
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 | 0.002 |
| Meta-epidemiology (narrow) | 0.001 | 0.000 |
| Meta-epidemiology (broad) | 0.001 | 0.001 |
| Bibliometrics | 0.001 | 0.001 |
| Science and technology studies | 0.000 | 0.001 |
| Scholarly communication | 0.002 | 0.002 |
| Open science | 0.001 | 0.001 |
| Research integrity | 0.003 | 0.004 |
| Insufficient payload (model declined to judge) | 0.007 | 0.003 |
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".