Cancer and Venous Thromboembolic Disease: From Molecular Mechanisms to Clinical Management
Bibliographic record
Abstract
Venous thromboembolism (vte) represents a major challenge in the management of patients with cancer. The malignant phenotype is associated with derangements in the coagulation cascade that can manifest as thrombosis, hemorrhage, or disseminated intravascular coagulation. The risk of vte is increased by a factor of approximately 6 in patients with cancer compared with non-cancer patients, and cancer patients account for approximately 20% of all newly diagnosed cases of vte. Postmortem studies have demonstrated rates of vte in patients with cancer to be as high as 50%. Despite that prevalence, vte prophylaxis is underused in hospitalized patients with cancer. Studies have demonstrated that hospitalized patients with cancer are less likely than their non-cancer counterparts to receive vte prophylaxis. Consensus guidelines address the aforementioned issues and emerging concepts in the area, including the use of risk-assessment models, biomarkers to identify patients at highest risk of vte, and use of anticoagulants as anticancer therapy. Despite those guidelines, a gulf exists between current recommendations and clinical practice; greater efforts are thus required to ensure effective implementation of strategies to reduce the incidence of vte in patients with cancer.
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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 | 0.002 |
| Meta-epidemiology (narrow) | 0.001 | 0.000 |
| Meta-epidemiology (broad) | 0.001 | 0.001 |
| 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 | 0.001 | 0.002 |
| Insufficient payload (model declined to judge) | 0.003 | 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".