Markers of Coagulation and Angiogenesis in Cancer-Associated Venous Thromboembolism
Bibliographic record
Abstract
PURPOSE: We sought to determine whether venous thromboembolism in cancer patients is associated with aberrant plasma levels of hemostatic and angiogenic factors. PATIENTS AND METHODS: Peripheral blood was collected before anticoagulant therapy from cancer patients with acute deep venous thrombosis (DVT; DVT + cancer group, n = 32), those without DVT (cancer control group, n = 36), and patients with acute DVT but no cancer (DVT control group, n = 58). Plasma assays of activation and inhibition of coagulation and fibrinolysis, as well as angiogenesis activation, were then performed. RESULTS: Median levels of thrombin-antithrombin complex, prothrombin fragments 1 + 2, and von Willebrand factor antigen were significantly greater in the DVT + cancer group than in the cancer control and DVT control groups (17.8 ng/mL v 4.6 ng/mL and 9.8 ng/mL, P =.0001 and P =.003, respectively; 3.65 nmol/L v 1.60 nmol/L and 2.71 nmol/L, P <.0001 and P =.011, respectively; and 4.04 U/mL v 2.26 U/mL and 2.06 U/mL, P <.0001, respectively). Median levels of tissue-type plasminogen activator were also significantly higher, while protein C activity was lower in the DVT + cancer group than in the DVT control group (14.6 ng/mL v 9.50 ng/mL, respectively, P =.0005; 0.89 U/mL v 1.11 U/mL, respectively, P =.0008). CONCLUSION: These data not only support prior observations of coagulation activation in patients with malignancy, but also provide new evidence for enhanced coagulation activation in the setting of acute venous thromboembolism in cancer. Future prospective studies are warranted to determine whether these and other potential markers of hypercoagulability may help to identify cancer patients at highest risk for venous thromboembolism.
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 | 0.001 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.001 | 0.001 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.000 | 0.000 |
| Open science | 0.000 | 0.000 |
| Research integrity | 0.000 | 0.000 |
| Insufficient payload (model declined to judge) | 0.001 | 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 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".