Engaging Patients in the Canadian Real-World Evidence for Value in Cancer Drugs (CanREValue) Initiative: Processes and Lessons Learned
Bibliographic record
Abstract
The Canadian Real-world Evidence for Value in Cancer Drugs (CanREValue) Collaboration established the Engagement Working Group (WG) to ensure that all key stakeholders had an opportunity to provide input into the development and implementation of the CanREValue Real-World Evidence (RWE) Framework. Two consultations were held in 2021 to solicit patient perspectives on key policy and data access issues identified in the interim policy and data WG reports. Over 30 individuals, representing patients, caregivers, advocacy leaders, and individuals engaged in patient research were invited to participate. The consultations provided important feedback and valuable lessons in patient engagement. Patient leaders actively shaped the process and content of the consultation. Breakout groups facilitated by patient advocacy leaders gave the opportunity for open and thoughtful contributions from all participants. Important recommendations were made: the RWE framework should not impede access to new drugs; it should be used to support conditional approvals; patient relevant endpoints should be captured in provincial datasets; access to data to conduct RWE should be improved; and privacy issues must be considered. The manuscript documents the CanREValue experience of engaging patients in a consultative process and the useful contributions that can be achieved when the processes to engage are guided by patients themselves.
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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 | 0.309 | 0.273 |
| Meta-epidemiology (narrow) | 0.001 | 0.001 |
| Meta-epidemiology (broad) | 0.002 | 0.002 |
| Bibliometrics | 0.003 | 0.004 |
| Science and technology studies | 0.028 | 0.021 |
| Scholarly communication | 0.024 | 0.009 |
| Open science | 0.007 | 0.030 |
| Research integrity | 0.013 | 0.027 |
| Insufficient payload (model declined to judge) | 0.006 | 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".