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.
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 | 0.000 | 0.001 |
| Meta-epidemiology (narrow) | 0.001 | 0.000 |
| Meta-epidemiology (broad) | 0.001 | 0.000 |
| Bibliometrics | 0.002 | 0.002 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.001 | 0.001 |
| Open science | 0.001 | 0.000 |
| Research integrity | 0.001 | 0.001 |
| 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".