Bibliographic record
Abstract
Robust evidence remains scarce in guiding best practice in the prevention and treatment of venous thromboembolism in patients living with cancer. Recommendations from major consensus guidelines are largely based on extrapolated data from trials performed mostly in noncancer patients, observational studies and registries, studies using surrogate outcomes, and underpowered randomized controlled trials. Nonetheless, a personalized approach based on individual risk assessment is uniformly recommended for inpatient and outpatient thromboprophylaxis and there is consensus that anticoagulant prophylaxis is warranted in selected patients with a high risk of thrombosis. Prediction tools for estimating the risk of thrombosis in the hospital setting have not been validated, but the use of prophylaxis in the ambulatory setting in those with a high Khorana score is under active investigation. Symptomatic and incidental thrombosis should be treated with anticoagulant therapy, but little is known about the optimal duration. Pharmacologic options for prophylaxis and treatment are still restricted to unfractionated heparin, low molecular weight heparin, and vitamin K antagonists because there is currently insufficient evidence to support the use of target-specific, non-vitamin K-antagonist oral anticoagulants. Although these agents offer practical advantages over traditional anticoagulants, potential drug interaction with chemotherapeutic agents, gastrointestinal problems, hepatic and renal impairment, and the lack of rapid reversal agents are important limitations that may reduce the efficacy and safety of these drugs in patients with active cancer. Clinicians and patients are encouraged to participate in clinical trials to advance the care of patients with cancer-associated thrombosis.
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 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) | 0.001 | 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 itClassification
machine, unvalidatedMachine predicted; a candidate call from one teacher head, 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".