Thrombosis and Cancer: The Role of Screening for Occult Cancer and Recognizing the Underlying Biological Mechanisms
Bibliographic record
Abstract
The association between cancer and thrombosis is well recognized. What is not known, however, is the exact relationship between these two common medical conditions. Although the development of venous thromboembolism (VTE) in a patient with known cancer is the most common presentation, in some patients, VTE may precede the diagnosis of malignancy by many months. The variation in clinical presentation is likely due to the heterogeneous biology of different tumor types and also reflects the limitations of detection or available diagnostic methods. Accumulating evidence now suggests that critical oncogenic events may also trigger activation of the coagulation cascade, leading to a prothrombotic environment that not only manifests as venous thromboembolic disease but also promotes the growth and progression of the malignancy. This chapter will review the evidence for screening for occult malignancy in patients presenting with unprovoked or idiopathic thrombosis, briefly outline the known biological relationships between malignancy and thrombosis, and summarize the clinical data on the potential anticancer effects of low molecular weight heparins (LMWHs).
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 | 0.001 | 0.002 |
| Meta-epidemiology (narrow) | 0.001 | 0.000 |
| Meta-epidemiology (broad) | 0.002 | 0.000 |
| Bibliometrics | 0.003 | 0.003 |
| Science and technology studies | 0.000 | 0.001 |
| Scholarly communication | 0.001 | 0.002 |
| Open science | 0.001 | 0.001 |
| Research integrity | 0.002 | 0.002 |
| 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".