Bibliographic record
Abstract
Cancer patients are in a hypercoagulable state. The pathogenesis of thrombosis in malignancy is multifactorial with mechanisms including release of procoagulants by tumour cells, comorbid predisposing factors (bed rest, infection, surgery, etc.) and anti-cancer drugs. Cancer patients with established venous thromboembolism are more likely to develop recurrent venous thromboembolism during treatment with oral anticoagulants. This paper reviews the use of heparin for the treatment of thrombotic disorders in cancer patients. Treatment of acute venous thrombosis comprises initial heparin administration, which for a cancer patient should last for at least 5 days, followed by administration of oral anticoagulants. Low-molecular-weight heparins (LMWHs) have been shown to be as safe and effective as standard heparin for the treatment of acute deep vein thrombosis. Recent meta-analyses have revealed lower mortality rates with LMWH than with standard heparin, indicating that LMWH may exert an inhibitory effect on tumour growth that is not observed with standard heparin.
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 | 0.001 |
| Bibliometrics | 0.002 | 0.002 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.000 | 0.000 |
| 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".