Differential biomarker profiles between unprovoked venous thromboembolism and cancer
Bibliographic record
Abstract
BACKGROUND: The relationship between cancer and venous thromboembolic disease (VTD) are complex because the activated coagulation factors are not only involved in thrombosis but also in malignant processes, such as angiogenesis and metastasis. OBJECTIVE: To compare phenotypes of extracellular vesicles (EVs), and levels of D-dimer, soluble P-selectin (sP-selectin) and antigenic tissue factor (TF) between unprovoked VTD patients, who did not develop cancer during one-year follow-up, and those with advanced stage of cancer but not associated with VTD. METHODS: A prospective study in which we included 138 unprovoked VTD patients and 67 advanced cancer patients, who did not develop thrombosis. Levels of EVs of different cellular origin (platelet, endothelium and leukocyte), EVs positive for tissue factor (TF) and P-selectin glycoprotein ligand 1 were quantified by flow cytometry. D-dimer, soluble P-selectin (sP-selectin) and antigenic TF were determined by ELISA. RESULTS: TF-positive EVs, D-dimer, and sP-selectin were markedly elevated in unprovoked VTD patients compared to cancer patients without association with thrombosis. CONCLUSIONS: : Circulating EVs, specifically TF-positive EVs, in combination with plasmatic markers of hypercoagulable states, such as D-dimer, sP-selectin and antigen TF, are able to discriminate between cancer patients without thrombosis and patients with unprovoked VTD. Research fields could be opened. Future studies will assess if these biomarkers together serve as predicting thrombotic events in cancer populations.
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How this classification was reachedexpand
Full frame distilled prediction
Teacher imitationNot calibrated prevalence, not ground truth. Human validation pending. Learned from the 10,348 direct Codex labels and 10,348 direct Gemma labels. Candidate is the union of thresholded teacher heads; consensus is their intersection. These outputs are machine_predicted_unvalidated and are not human labels or direct frontier model labels.
Codex and Gemma teacher scores by category
| Category | Codex | Gemma |
|---|---|---|
| Metaresearch | 0.000 | 0.000 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.001 | 0.000 |
| Bibliometrics | 0.000 | 0.000 |
| 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 teacher head, 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".