Direct Oral Anticoagulants for the Treatment of Venous Thromboembolic Events: Economic Evaluation
Bibliographic record
Abstract
In order to inform policy work and clinical decisions, a health technology assessment was undertaken by CADTH. For this project CADTH worked in partnership with the Canadian Collaboration for Drug Safety, Effectiveness and Network Meta-Analysis (ccNMA), funded by the Drug Safety and Effectiveness Network (DSEN) of the Canadian Institutes of Health Research (CIHR). The health technology assessment includes both a clinical and an economic evaluation. The clinical component was conducted by ccNMA, and the economic evaluation was conducted by CADTH. This report provides findings from the economic evaluation.The economic evaluation was undertaken to inform the rational use of direct oral anticoagulants (DOACs) and ensure health care system sustainability; it aimed to determine the cost-effectiveness of anticoagulation treatment strategies for patients with venous thromboembolic events (VTEs). This was done in close collaboration with ccNMA, which conducted a rigorous systematic review of randomized controlled trials and a network meta-analysis (NMA) used to inform the relative efficacy and safety in the economic evaluation. Findings from the economic evaluation are presented in this report; the results of the clinical evaluation are available at: https://www.ottawaheart.ca/researchers/resources-services/core-facilities/cardiovascular-research-methods-centre
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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 | 0.139 | 0.291 |
| Meta-epidemiology (narrow) | 0.002 | 0.001 |
| Meta-epidemiology (broad) | 0.005 | 0.007 |
| Bibliometrics | 0.007 | 0.007 |
| Science and technology studies | 0.001 | 0.001 |
| Scholarly communication | 0.005 | 0.002 |
| Open science | 0.001 | 0.002 |
| Research integrity | 0.003 | 0.003 |
| Insufficient payload (model declined to judge) | 0.007 | 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".