Developing a framework to incorporate real-world evidence in cancer drug funding decisions: the Canadian Real-world Evidence for Value of Cancer Drugs (CanREValue) collaboration
Bibliographic record
Abstract
BACKGROUND: Oncology therapy is becoming increasingly more expensive and challenging the affordability and sustainability of drug programmes around the world. When new drugs are evaluated, health technology assessment organisations rely on clinical trials to inform funding decisions. However, clinical trials are not able to assess overall survival and generalises evidence in a real-world setting. As a result, policy makers have little information on whether drug funding decisions based on clinical trials ultimately yield the outcomes and value for money that might be expected. OBJECTIVE: The Canadian Real-world Evidence for Value of Cancer Drugs (CanREValue) collaboration, consisting of researchers, recommendation-makers, decision makers, payers, patients and caregivers, are developing and testing a framework for Canadian provinces to generate and use real-world evidence (RWE) for cancer drug funding in a consistent and integrated manner. STRATEGY: The CanREValue collaboration has established five formal working groups (WGs) to focus on specific processes in the generation and use of RWE for cancer drug funding decisions in Canada. The different RWE WGs are: (1) Planning and Drug Selection; (2) Methods; (3) Data; (4) Reassessment and Uptake; (5) Engagement. These WGs are acting collaboratively to develop a framework for RWE evaluation, validate the framework through the multiprovince RWE projects and help to integrate the final RWE framework into the Canadian healthcare system. OUTCOMES: The framework will enable the reassessment of cancer drugs, refinement of funding recommendations and use of novel funding mechanisms by decision-makers/payers across Canada to ensure the healthcare system is providing clinical benefits and value for money.
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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 | 0.030 | 0.024 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.001 | 0.000 |
| Bibliometrics | 0.001 | 0.003 |
| Science and technology studies | 0.001 | 0.000 |
| Scholarly communication | 0.001 | 0.001 |
| Open science | 0.002 | 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; both teacher heads agree on what is shown here.
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".