A randomized phase II trial of a new anticoagulant, apixaban, in metastatic cancer
Bibliographic record
Abstract
e20514 Background: Cancer patients receiving chemotherapy, biologic, and molecular targeted therapies are at increased risk of venous thromboembolism (VTE). Currently available anticoagulants (ACs) are not well suited to prevent VTE in such patients. Vitamin K antagonist oral ACs require frequent lab monitoring and low molecular weight heparins require daily subcutaneous injection. There is concern for bleeding with ACs. Apixaban (A) is a new antithrombotic agent which inhibits activated coagulation Factor X, is taken orally, and does not require lab monitoring. Trials to prevent post-operative VTE in orthopedic surgery showed that A was effective and safe. We wanted to assess the feasibility of A in cancer. Methods: In a randomized phase II trial, patients with metastatic cancer on 1st or 2nd line chemotherapy received study drug once daily for 12 weeks; either 5, 10 or 20 mg of A, or placebo. The primary outcome measure was the proportion of patients remaining free of major bleeding (MB), clinically relevant non-major bleeding (CRNMB), VTE, and grade ≥3 adverse events considered to be probably/definitely related to study drug (AE*). After 125 patients were recruited, the sponsor eliminated further randomization to the 10 and 20 mg arms, to add experience with the 5 mg dose currently under evaluation for VTE prevention in other conditions. Data on the first 125 patients are reported. Results: The study population was 50% male; 88% had ECOG performance status 0 or 1. The most common cancers were breast, colon, pancreas, and myeloma. 23% had liver metastases. Approximately 80% of A patients completed 12 weeks of treatment. The numbers of patients with events were: Conclusions: Apixiban was well tolerated in patients with advanced cancer on chemotherapy. Major bleeding, thrombosis, and drug-related SAEs were very low. These results support further study of A in phase III trials for VTE prevention in cancer patients. [Table: see text] [Table: see text]
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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 | 0.002 | 0.002 |
| Meta-epidemiology (narrow) | 0.002 | 0.001 |
| Meta-epidemiology (broad) | 0.002 | 0.001 |
| Bibliometrics | 0.000 | 0.001 |
| Science and technology studies | 0.000 | 0.001 |
| Scholarly communication | 0.001 | 0.001 |
| Open science | 0.001 | 0.000 |
| Research integrity | 0.001 | 0.002 |
| Insufficient payload (model declined to judge) | 0.009 | 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".