Platelets, Coagulation and Cancer: Multifaceted Interactions
Bibliographic record
Abstract
Approach: Literature review of the multifaceted interactions between platelets, coagulation and cancer.Results: Over the years, the links existing between cancer development, progression and occurrence of metastasis on one side and coagulation on the other have become obvious.Tumors seems to activate platelets whereas, platelets, on the other hand, through their capacity to activate and release soluble factors and microparticles, interact with tumor cells and influence immune regulation.They appear to be key regulators of many cancer events.Furthermore, coagulation with its different facets also interplays and significantly crosstalks with malignancy.The objectives of this article are to review the mechanisms through which cancer interacts with platelets and the coagulation, triggering thrombosis and the role played by platelets and coagulation factors in the regulation of cancer and to underline the perspectives that are now open in the development of novel diagnostic tools and new cancer treatment strategies.Conclusion/Recommendations: Challenging issues and unresolved questions still need to be addressed to understand the complexity existing between coagulation factors and platelet components and the different stages of cancer progression.Recent discoveries are leading clinicians to consider new therapeutic applications of anticoagulant therapies or new drugs targeting specific platelet functions in cancer patients' management.Furthermore, markers of coagulation and platelet activity may prove to serve as biomarkers for dormant tumors.
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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 | 0.001 | 0.001 |
| Meta-epidemiology (narrow) | 0.001 | 0.000 |
| Meta-epidemiology (broad) | 0.001 | 0.001 |
| Bibliometrics | 0.004 | 0.003 |
| Science and technology studies | 0.000 | 0.001 |
| Scholarly communication | 0.002 | 0.002 |
| Open science | 0.001 | 0.001 |
| Research integrity | 0.002 | 0.002 |
| Insufficient payload (model declined to judge) | 0.004 | 0.001 |
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".