Why this work is in the frame
A frame that forgets how it found something cannot be audited. These are the routes that admitted this work.
Bibliographic record
Abstract
Thrombin-specific inhibitors directly diminish thrombin-induced coagulation and cellular activities without the side effects of heparin. Hirudin is the most potent natural thrombin-specific inhibitor. Recombinant hirudins (such as desirudin) have been shown to be effective in the treatment of heparin-induced thrombocytopenia (HIT) and in the prevention of thrombotic complications after hip or knee surgery. The application of recombinant hirudin has been limited mainly by hemorrhagic complications. Synthetic thrombin-specific inhibitors, including oligopeptides, tripeptides and non-peptide low molecular weight (LMW) thrombin inhibitors, have been designed according to their interactions with the active sites of thrombin. Bivalirudin (an anti-thrombin oligopeptide) has been approved for preventing thrombosis in unstable angina patients following angioplasty in adjunct to aspirin. Argotroban (a tripeptide thrombin inhibitor) 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. A number of LMW thrombin-specific inhibitors have been developed. Some of them can be administrated orally, and cause less increase in bleeding time than other thrombin inhibitors. The efficacy, safety, stability and oral bioavailability of the thrombin inhibitors may be considerably improved through structural optimization. Most of the LMW thrombin inhibitors are currently being tested in animal models or at early stages of clinical trials. In this review, we will present an overview of recent advances in thrombin-specific inhibitors.
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.
Full frame distilled prediction
Teacher imitationNot calibrated prevalence, not ground truth. Human validation pending. Learned from the 10,348 direct Codex labels and 10,348 direct Gemma labels. Candidate is the union of thresholded teacher heads; consensus is their intersection. These outputs are machine_predicted_unvalidated and are not human labels or direct frontier model labels.
Codex and Gemma teacher scores by category
| Category | Codex | Gemma |
|---|---|---|
| Metaresearch | 0.001 | 0.000 |
| Meta-epidemiology (narrow) | 0.001 | 0.001 |
| Meta-epidemiology (broad) | 0.007 | 0.004 |
| Bibliometrics | 0.000 | 0.001 |
| Science and technology studies | 0.000 | 0.001 |
| Scholarly communication | 0.000 | 0.000 |
| Open science | 0.001 | 0.001 |
| Research integrity | 0.001 | 0.001 |
| Insufficient payload (model declined to judge) | 0.000 | 0.000 |
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 it