Updates in the Incidence, Pathogenesis, and Management of Cancer and Venous Thromboembolism
Bibliographic record
Abstract
Patients with cancer are at higher risk of developing venous thromboembolism (VTE) compared with the general population. This elevated risk is due to several risk factors and multiple, overlapping thrombotic and hemostatic pathophysiological pathways that are specific to this patient population. Hence, the management of cancer-associated VTE can be challenging for clinicians. Patients with cancer-associated VTE are at higher risk of both recurrent events despite anticoagulation and bleeding complications due to the anticoagulant regimens. Direct oral anticoagulants have recently been shown to be effective, safe, and more convenient than parenteral low-molecular-weight heparin for the management of cancer-associated VTE. Despite these recent advances in anticoagulant therapy, many unmet needs remain in these patients (increased risk of bleeding with specific cancer types, drug-drug interactions, liver dysfunction). Factor XI inhibitors are currently being assessed for the management of cancer-associated VTE and may help clinicians address these important knowledge gaps.
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 | 0.003 |
| Meta-epidemiology (narrow) | 0.001 | 0.000 |
| Meta-epidemiology (broad) | 0.001 | 0.001 |
| Bibliometrics | 0.003 | 0.002 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.001 | 0.002 |
| Open science | 0.001 | 0.001 |
| Research integrity | 0.001 | 0.002 |
| Insufficient payload (model declined to judge) | 0.007 | 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".