Low-molecular-weight-heparin versus a coumarin for the prevention of recurrent venous thromboembolism in high- and low-risk patients with active cancer: a post hoc analysis of the CLOT Study
Bibliographic record
Abstract
In patients with active cancer and acute venous thromboembolism (VTE), the low-molecular-weight-heparin (LMWH) dalteparin is more effective than vitamin K antagonist (VKA) in reducing the risk of recurrent venous thromboembolism (rVTE) without increasing the risk of bleeding. However, the relative benefit of LMWH versus VKA in patients with active cancer at high or low risk of rVTE and bleeding is unclear. This post hoc analysis used data from the CLOT study to explore the efficacy and safety of LMWH versus VKA in preventing recurrent thrombosis in high- and low-risk patients with active cancer. High-risk patients were defined by metastatic disease and/or antineoplastic treatment at baseline; low-risk patients presented with neither. Among high-risk patients, rVTE occurred in 25/318 (8%) (LMWH) versus 53/314 (17%) (VKA) (hazard ratio, 0.44; p = 0.001). No significant difference was detected in the rate of major or any bleeding. The 6-month mortality rate was 40% (LMWH) versus 41% (VKA). In low-risk patients, 2/20 (10%) (LMWH) had rVTE versus 0/24 (0%) (VKA) (hazard ratio, not estimable; p = 0.998). No significant difference was detected in the rate of major or any bleeding. The 6-month mortality rate was 20% (LMWH) versus 29% (VKA). In patients with cancer-associated thrombosis at high risk of rVTE and bleeding, the LMWH dalteparin was more effective than VKA in reducing the risk of rVTE without increasing the risk of bleeding. No difference in rate of rVTE or bleeding was observed between LMWH and VKA among low-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 | 0.006 | 0.006 |
| Meta-epidemiology (narrow) | 0.002 | 0.001 |
| Meta-epidemiology (broad) | 0.005 | 0.007 |
| Bibliometrics | 0.000 | 0.000 |
| Science and technology studies | 0.000 | 0.001 |
| Scholarly communication | 0.001 | 0.001 |
| Open science | 0.001 | 0.001 |
| Research integrity | 0.002 | 0.003 |
| Insufficient payload (model declined to judge) | 0.003 | 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 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".