Hypercoagulability and Thrombosis Risk in Prostate Cancer: The Role of Thromboelastography
Bibliographic record
Abstract
Thrombosis is one of the leading causes of death in cancer. Cancer-induced hypercoagulable state contributes to thrombosis and is often overlooked. Prostate cancer may not be of high thrombogenic potential compared with other cancers, but its high prevalence brings it into focus. Pathological evidence for venous thromboembolisms (VTEs) in prostate cancer exists. Factors such as age, comorbidities, and therapies increase the VTE risk further. There is a need to systematically identify the risk of VTE in regard to patient-, cancer-, and treatment-related factors to risk stratify patients for better-targeted and individualized strategies to prevent VTE. Sensitive tests to enable such risk assessment are urgently required. There is sufficient evidence for the utility of thromboelastography (TEG) in cancer, but it is not yet part of the clinic and there is only limited data on the use of TEG in prostate cancer. One study revealed that compared with age-matched controls, 68.8% of prostate cancer patients demonstrated hypercoagulable TEG parameters. The absence of clinical guidelines is a limiting factor in TEG use in the cancer population. Cancer heterogeneity and the unique cancer-specific microenvironment in each patient, as well as determining the hypercoagulable state in each patient, are added limitations. The way forward is to combine efforts to design large multicenter studies to investigate the utility and clinical effectiveness of TEG in cancer and establish longitudinal studies to understand the link between hypercoagulable state and development of thrombosis. There is also a need to study low thrombogenic cancers as well as high thrombogenic ones. Awareness among clinicians and understanding of test applicability and interpretation are needed. Finally, expert discussion is critical to identify the investigation priorities.
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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 | 0.005 | 0.011 |
| Meta-epidemiology (narrow) | 0.001 | 0.000 |
| Meta-epidemiology (broad) | 0.001 | 0.001 |
| Bibliometrics | 0.002 | 0.003 |
| Science and technology studies | 0.000 | 0.001 |
| Scholarly communication | 0.002 | 0.002 |
| Open science | 0.001 | 0.001 |
| Research integrity | 0.001 | 0.002 |
| Insufficient payload (model declined to judge) | 0.002 | 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".