Developing a Canadian Real-World Evidence Action Plan across the Drug Life Cycle
Bibliographic record
Abstract
Policy makers face challenges with the number of drugs for rare indications and rapidly rising costs. In facing these challenges, decision-makers see real-world evidence (RWE) as an opportunity. Health Canada and the Canadian Agency for Drugs and Technologies in Health (CADTH) recently announced their intent to co-develop an action plan to optimize the process for the systematic use and integration of RWE into both regulatory and reimbursement decision-making in Canada. When implemented, this will have a significant impact on how drugs are approved and paid for in Canada. We highlight the key opportunities, barriers and future directions related to the use of RWE throughout the life cycle of drugs in Canada.
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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 | 0.223 | 0.349 |
| Meta-epidemiology (narrow) | 0.002 | 0.002 |
| Meta-epidemiology (broad) | 0.003 | 0.005 |
| Bibliometrics | 0.012 | 0.009 |
| Science and technology studies | 0.008 | 0.005 |
| Scholarly communication | 0.022 | 0.009 |
| Open science | 0.009 | 0.012 |
| Research integrity | 0.021 | 0.020 |
| Insufficient payload (model declined to judge) | 0.010 | 0.002 |
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; the direct Gemma label and the distilled Codex classifier 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".