Bibliographic record
Abstract
For more than 50 years, heparin and vitamin K antagonists (VKAs) have been the anticoagulant drugs used to prevent and treat thrombosis. Low molecular weight heparins (LMWHs) are more recent and have been available for approximately 20 years. Patients with cancer are members of a unique patient population because of their high risk for thrombosis and the risk of anticoagulant-related bleeding. With the currently available antithrombotic agents, patients with cancer still have unmet needs in terms of the prevention and treatment of thrombosis. Although long-term LMWH is the treatment of choice for patients with cancer who have acute, symptomatic venous thromboembolism (VTE), some patients still experience recurrent VTE. More effective antithrombotic agents are needed for such patients. Convenient (ie, oral and with no laboratory monitoring), effective, and safe agents are needed to prevent thrombosis in patients taking chemotherapy and antiangiogenic drugs and in patients with central vein catheters. There are a number of new antithrombotic agents that have been studied in recent years and will soon be available for certain diseases. They target either activated factor X (ie, factor Xa) or activated thrombin, and some of them have potential therapeutic value in patients with cancer. In this article, the clinical research model used for the development of a new antithrombotic agent is discussed along with the results of recent trials that evaluate these new agents in high-risk populations.
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 | 0.001 | 0.001 |
| Meta-epidemiology (narrow) | 0.001 | 0.000 |
| Meta-epidemiology (broad) | 0.001 | 0.001 |
| Bibliometrics | 0.002 | 0.003 |
| Science and technology studies | 0.000 | 0.001 |
| Scholarly communication | 0.001 | 0.002 |
| Open science | 0.001 | 0.001 |
| Research integrity | 0.001 | 0.002 |
| Insufficient payload (model declined to judge) | 0.005 | 0.002 |
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".