The role of low-molecular-weight heparins in the prevention and treatment of venous thromboembolism in cancer patients
Bibliographic record
Abstract
Accumulating evidence suggests that low-molecular-weight heparins are the drug of choice for the prevention and treatment of venous thromboembolism in patients with cancer. For prophylaxis in the surgical setting, once-daily subcutaneous injections of low-molecular-weight heparin are as effective and safe as multiple doses of unfractionated heparin. Extending prophylaxis with low-molecular-weight heparins beyond hospitalization was recently found to reduce safely the risk of postoperative thrombosis after abdominal surgery for cancer. For the long-term treatment of deep vein thrombosis and in select patients with pulmonary embolism, recently completed clinical trials have shown that secondary prophylaxis with low-molecular-weight heparin is feasible and more effective than oral anticoagulant therapy in preventing recurrent venous thromboembolism in cancer patients. There is also evidence that low-molecular-weight heparins are effective in cancer patients who develop recurrent thrombosis while on warfarin therapy. Lastly, the potential antineoplastic effects of low-molecular-weight heparins make these agents an attractive option in patients with cancer. Although the management of cancer patients with venous thromboembolism remains challenging, low-molecular-weight heparins have simplified and improved the prevention and treatment of venous thromboembolism in these high-risk patients.
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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 | 0.001 |
| 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".