Comparison between Canadian and British oncology drug review recommendations and their impact on patient access.
Bibliographic record
Abstract
6537 Background: Access and funding for oncology medications in Canada and the United Kingdom are influenced by recommendations released by the pan-Canadian Oncology Drug Review (pCODR) and the National Institute for Health and Care Excellence (NICE), respectively. This study investigated variations between these organizations’ recommendations and the clinical implications these differences had on access to medications. Methods: The pCODR Drug Review and NICE Technology Appraisal Guidance databases were reviewed to identify all oncological drug recommendations made by both agencies between 2008 and 2016. Recommendations were matched and then evaluated according to an algorithm to identify clinically relevant differences that restricted access in one jurisdiction relative to the other. Length of time from drug submission to final recommendation by NICE and pCODR were compared using Wilcoxon rank sum tests, as was the length of time between regional funding approval. Results: Between 2008 and 2016, 31 medication indications were evaluated by both pCODR and NICE. For 12 indications, funding was only supported by one agency. Eight indications were approved by both agencies, but with clinically relevant differences in wording, with each agency making a more restrictive recommendation four times. Average time from submission to recommendation was faster for pCODR than NICE (213.8 days vs. 407.9 days; p < 0.001) but the average time to funding decision (see table) was similar (410.1 days in Canada vs. 407.9 days in the United Kingdom; p = 0.71). Conclusions: Although clinically relevant differences in recommendations do exist between NICE and pCODR, neither agency was consistently more restrictive. pCODR recommendations were made more quickly than NICE, but this did not translate into faster funding approval in Canada. Jurisdictional barriers exist for cancer patients which could be mitigated through harmonization and acceleration of drug review processes. [Table: see text]
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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 | 0.044 | 0.376 |
| Meta-epidemiology (narrow) | 0.000 | 0.001 |
| Meta-epidemiology (broad) | 0.001 | 0.002 |
| Bibliometrics | 0.007 | 0.016 |
| Science and technology studies | 0.002 | 0.001 |
| Scholarly communication | 0.006 | 0.002 |
| Open science | 0.002 | 0.002 |
| Research integrity | 0.001 | 0.002 |
| 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".