Contribution of the Hemostatic System to Angiogenesis in Cancer
Bibliographic record
Abstract
It is now recognized that the hemostatic system plays an important role in cancer growth and dissemination, processes known to be vitally dependent on new tumor blood vessel formation (angiogenesis). There is also an increasing body of evidence supporting the link between the various components of the coagulation/fibrinolysis systems and angiogenic activity in cancer patients. Tissue factor (TF), thrombin, fibrinogen, fibrin, and plasminogen activation system, as well as platelets, all are able to promote angiogenesis. On the other hand, coagulation inhibitors, as well as cryptic (proteolytically released) domains of hemostatic proteins, are also known to act as angiogenesis inhibitors. Indeed, modulation (stimulation or inhibition) of angiogenesis may result from either classical functions of various molecular components of the hemostatic cascade, their less studied "alternative" activities, or both. Although much remains to be understood about this complex circuitry these considerations support the judicious use of anticoagulants in patients with malignancy as well as encourage the search for novel antiangiogenic activities that may reside within molecular and cellular components of the hemostatic system.
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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 | 0.003 |
| Science and technology studies | 0.000 | 0.001 |
| Scholarly communication | 0.001 | 0.001 |
| Open science | 0.001 | 0.000 |
| Research integrity | 0.001 | 0.001 |
| Insufficient payload (model declined to judge) | 0.003 | 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".