Treatment of Venous Thrombosis in the Cancer Patient
Why this work is in the frame
A frame that forgets how it found something cannot be audited. These are the routes that admitted this work.
Bibliographic record
Abstract
Venous thromboembolism is a common complication in patients with cancer. The management of deep vein thrombosis and pulmonary embolism can be a considerable challenge in patients with cancer. The cancer itself and associated treatments contribute to an ongoing thrombogenic stimulus, while cancer patients are thought to be at increased risk for anticoagulant-induced bleeding. Initial treatment of acute thromboembolism is with intravenous unfractionated heparin or subcutaneous low molecular weight heparin. Treatment at home with low molecular weight heparin is an attractive option in patients with malignant disease. Long-term treatment of acute venous thromboembolism has traditionally been with oral anticoagulants. However, the inconvenience and narrow therapeutic window of oral anticoagulants make such therapy unattractive and problematic in cancer patients. Low molecular weight heparins are being evaluated as an alternative for long-term therapy because their anticoagulant effects are more predictable and laboratory monitoring is unnecessary. Although many clinical issues remain unresolved in the treatment of cancer patients with venous thromboembolism, the future holds much promise as new antithrombotic agents, including factor Xa antagonists and oral thrombin inhibitors, are being tested in clinical trials.
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.
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) | 0.004 | 0.001 |
| Bibliometrics | 0.000 | 0.001 |
| 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) | 0.000 | 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 it