VP02 Real-World Evidence (RWE) And CADTH Pan-Canadian Oncology Drug Review
Bibliographic record
Abstract
Introduction: The pan-Canadian Oncology Drug Review (pCODR) program was established by Canada's provincial and territorial Ministries of Health (except Quebec) to assess cancer drug therapies and make recommendations to guide drug reimbursement decisions. The pCODR Expert Review Committee (pERC) makes reimbursement recommendations, providing a rationale for the recommendation and next steps for stakeholders. The objective of this analysis was to identify reviews and reasons pERC has requested real-world evidence (RWE) data collection. Methods: A retrospective analysis of pERC Final Recommendations (January 2012 – May 2017) was conducted. pERC Final Recommendations include drug information, reimbursement recommendation, rationale for recommendation following pERC's Deliberative Framework (clinical benefit, patient-based values, economic evaluation, and adoption feasibility), next steps for jurisdictions to consider to support their funding decisions, summary of deliberations, and evidence in brief. Reviews were included if there was a next step advising the collection of RWE to reduce uncertainty in the drug under review. Results: Out of eighty-four reviews, forty-one (forty-eight percent) included a next step to collect RWE to address a gap in the available evidence. Reasons for RWE data collection, in descending order of frequency, were to inform: sequencing of available therapies; magnitude of clinical benefit and cost-effectiveness or the true cost-effectiveness; duration of treatment and cost-effectiveness; defining the population or disease progression; quality of life; and dosage. Conclusions: In almost half of pERC's recommendation there is an indication that there is a gap in the existing evidence that could potentially be addressed through the collection of RWE. This reflects the rising number of new cancer drugs, limited evidence supporting submissions (for example non-comparative studies), and newer drugs such as immunotherapies which may not have a fixed treatment duration. Further research includes development of mechanisms for RWE data collection to help inform pERC recommendations and assist stakeholders with adoption feasibility of reviewed drugs.
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 | 0.117 | 0.432 |
| Meta-epidemiology (narrow) | 0.001 | 0.002 |
| Meta-epidemiology (broad) | 0.003 | 0.004 |
| Bibliometrics | 0.026 | 0.029 |
| Science and technology studies | 0.004 | 0.004 |
| Scholarly communication | 0.019 | 0.005 |
| Open science | 0.004 | 0.004 |
| Research integrity | 0.005 | 0.004 |
| Insufficient payload (model declined to judge) | 0.019 | 0.004 |
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".