Bibliographic record
Abstract
Cancer and its treatments are well-recognized risk factors for venous thromboembolism (VTE). Evidence suggests that the absolute risk depends on the tumor type, the stage or extent of the cancer, and treatment with antineoplastic agents. Furthermore, age, surgery, immobilization, and other comorbid features will also influence the overall likelihood of thrombotic complications, as they do in patients without cancer. The role of hereditary thrombophilia in patients with cancer and thrombosis is still unclear, and screening for this condition in cancer patients is not indicated. The most common malignancies associated with thrombosis are those of the breast, colon, and lung, reflecting the prevalence of these malignancies in the general population. When adjusted for disease prevalence, the cancers most strongly associated with thrombotic complications are those of the pancreas, ovary, and brain. Idiopathic thrombosis can be the first manifestation of an occult malignancy. However, intensive screening for cancer in patients with VTE often does not improve survival and is not generally warranted. Independently of the timing of cancer diagnosis (before or after the VTE), the life expectancy of cancer patients with VTE is relatively short, because of both deaths from recurrent VTE and the cancer itself. Patients with cancer and acute VTE who take anticoagulants for an extended period are at increased risk of recurrent VTE and bleeding. A recent randomized trial, the Randomized Comparison of Low Molecular Weight Heparin versus Oral Anticoagulant Therapy for Long-Term Anticoagulation in Cancer Patients with Venous Thromboembolism (CLOT) study, showed that low molecular weight heparin may be a better treatment option for this group of patients. The antineoplastic effects of anticoagulants are being actively investigated with promising preliminary results.
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 | 0.002 | 0.009 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.001 | 0.001 |
| Bibliometrics | 0.001 | 0.004 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.001 | 0.001 |
| Open science | 0.000 | 0.001 |
| Research integrity | 0.001 | 0.002 |
| 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".