Treatment Algorithm in Cancer-Associated Thrombosis: Canadian Expert Consensus
Bibliographic record
Abstract
Management of anticoagulant therapy for the treatment of venous thromboembolism (vte) in cancer patients is complex because of an increased risk of recurrent vte and major bleeding complications in those patients relative to the general population. Subgroups of patients with cancer also show variation in their risk for recurrent vte and adverse bleeding events. Accordingly, a committee of 10 Canadian clinical experts developed the consensus risk- stratification treatment algorithm presented here to provide guidance on tailoring anticoagulant treatment choices for the acute and extended treatment of symptomatic and incidental vte, to prevent recurrent vte, and to minimize the bleeding risk in patients with cancer. During a 1-day live meeting, a systematic review of the literature was performed, and a draft treatment algorithm was developed. The treatment algorithm was refined through the use of a Web-based platform and a series of online teleconferences. Clinicians using this treatment algorithm should consider the bleeding risk, the type of cancer, and the potential for drug-drug interactions in addition to informed patient preference in determining the most appropriate treatment for patients with cancer-associated thrombosis. Anticoagulant therapy should be regularly reassessed as the patient's cancer status and management change over time.
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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) | 0.001 | 0.001 |
| Meta-epidemiology (broad) | 0.004 | 0.001 |
| Bibliometrics | 0.001 | 0.001 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.000 | 0.000 |
| Open science | 0.000 | 0.000 |
| Research integrity | 0.001 | 0.000 |
| Insufficient payload (model declined to judge) | 0.001 | 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".