Drug approval status and recommendations for listing on public formularies: a Canadian cohort analysis
Bibliographic record
Abstract
OBJECTIVES: Investigate if the recommendations by the Common Drug Review (CDR) and the pan-Canadian Oncology Drug Review (pCODR) to provincial, territorial and federal drug plans about whether to list non-oncology and oncology drug-indication combinations on their formularies are associated with whether the drug-indication combination was approved via the standard evidence pathway or the Notice of Compliance with conditions (NOC/c-limited evidence) pathway. DESIGN: Cohort study. DATA SOURCES: Websites of the CDR and pCODR up to the end of 31 March 2017; journal articles evaluating drugs approved through the NOC/c pathway, the NOC database, the NOC/c website and the Summary Basis of Decision website. INTERVENTIONS: Recommendations by the CDR and pCODR. PRIMARY AND SECONDARY OUTCOME MEASURES: Analysis of the percent of drugs receiving positive listing recommendations from CDR and pCODR depending on the pathway used to approve the drug. RESULTS: There were 310 recommendations for drug-indication combinations from the CDR and 79 from the pCODR. There was a statistically significant difference in the number of drug-indication combinations that received a list versus do not list recommendation from the CDR for those approved through the standard pathway compared with those approved through the NOC/c pathway (p=0.0407). A similar analysis for recommendations from the pCODR was not statistically significant. CONCLUSION: For non-oncology drug-indication combinations, the type of review appears to influence the recommendation regarding listing on public formularies. This difference may reflect the level of evidence about the efficacy and safety of the drug indication at the time the recommendation was made.
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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 | 0.007 | 0.039 |
| Meta-epidemiology (narrow) | 0.001 | 0.001 |
| Meta-epidemiology (broad) | 0.001 | 0.003 |
| Bibliometrics | 0.005 | 0.013 |
| Science and technology studies | 0.003 | 0.001 |
| Scholarly communication | 0.003 | 0.002 |
| Open science | 0.003 | 0.002 |
| Research integrity | 0.001 | 0.002 |
| Insufficient payload (model declined to judge) | 0.007 | 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".