Bibliographic record
Abstract
Exposure of blood to tissue factor (TF) sets off the coagulation cascade. TF is a transmembrane protein that serves as an essential cofactor for activated coagulation factor VII (FVIIa). TF may be exposed locally by vascular injury (such as balloon angioplasty) or by spontaneous rupture of an atherosclerotic plaque. Expression of TF may also be induced on monocytes and endothelial cells in conditions like sepsis and cancer, causing a more generalised activation of clotting. TF may thus play a central role in thrombosis in a number of settings, and attention has turned to blocking TF as a means to prevent thrombosis. Inhibiting the inducible expression of TF by monocytes can be achieved by 'deactivating' cytokines, such as interleukin (IL)-4, -10 and -13, or by certain prostanoids; by drugs that modify signal transduction, such as pentoxifylline, retinoic acid or vitamin D(3), or by antisense oligonucleotides. Such approaches are for the most part at a preclinical stage. The function of TF can be blocked by antibodies that prevent the binding of FVIIa to TF; by active site-inhibited FVIIa, which competes with native FVIIa for binding; by antibodies or small molecules that block the function of the TF/FVIIa complex; and by molecules, such as TF pathway inhibitor or nematode anticoagulant peptide C2, which inhibit the active site of FVIIa in the TF/FVIIa complex after first binding to activated factor X. The latter two agents have entered Phase II clinical trials. Perhaps most intriguing is the use of anti-TF agents locally, which holds the promise of stopping thrombosis at a specific site of injury without the bleeding risk associated with systemic anticoagulation.
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 | 0.000 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.000 | 0.000 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.001 | 0.000 |
| Open science | 0.000 | 0.000 |
| Research integrity | 0.001 | 0.001 |
| Insufficient payload (model declined to judge) | 0.010 | 0.004 |
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".