Venous Thromboembolism and Cancer: Risks and Outcomes
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
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.
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) | 0.002 | 0.000 |
| Bibliometrics | 0.000 | 0.000 |
| 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