Development of a Multi-Criteria Decision Analysis Rating Tool to Prioritize Real-World Evidence Questions for the Canadian Real-World Evidence for Value of Cancer Drugs (CanREValue) Collaboration
Bibliographic record
Abstract
The Canadian Real-world Evidence for Value of Cancer Drugs (CanREValue) collaboration developed an MCDA rating tool to assess and prioritize potential post-market real-world evidence (RWE) questions/uncertainties emerging from public drug funding decisions in Canada. In collaboration with a group of multidisciplinary stakeholders from across Canada, the rating tool was developed following a three-step process: (1) selection of criteria to assess the importance and feasibility of an RWE question; (2) development of rating scales, application of weights and calculating aggregate scores; and (3) validation testing. An initial MCDA rating tool was developed, composed of seven criteria, divided into two groups. Group A criteria assess the importance of an RWE question by examining the (1) drug's perceived clinical benefit, (2) magnitude of uncertainty identified, and (3) relevance of the uncertainty to decision-makers. Group B criteria assess the feasibility of conducting an RWE analysis including the (1) feasibility of identifying a comparator, (2) ability to identify cases, (3) availability of comprehensive data, and (4) availability of necessary expertise and methodology. Future directions include partnering with the Canadian Agency for Drugs and Technology in Health's Provincial Advisory Group for further tool refinement and to gain insight into incorporating the tool into drug funding deliberations.
Fetched live from OpenAlex and de-inverted. Abstracts are not stored in this database: the inverted indexes are 8.6 GB of the frame’s 9.3 GB of text, and the host has 13 GB free.
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 | 0.142 | 0.261 |
| Meta-epidemiology (narrow) | 0.002 | 0.001 |
| Meta-epidemiology (broad) | 0.003 | 0.006 |
| Bibliometrics | 0.015 | 0.011 |
| Science and technology studies | 0.003 | 0.001 |
| Scholarly communication | 0.010 | 0.003 |
| Open science | 0.004 | 0.006 |
| Research integrity | 0.002 | 0.005 |
| Insufficient payload (model declined to judge) | 0.010 | 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".