Data from: Health Canada’s use of accelerated review pathways and therapeutic innovation, 1995-2016: a cohort study
Bibliographic record
Abstract
Objectives: This study examines the use of accelerated approval pathways by Health Canada over the period 1995 to 2016 inclusive and the relationship between the use of these pathways and the therapeutic gain offered by new products. Design: Cohort study. Data sources: Therapeutic Products Directorate, Biologics and Genetic Therapies Directorate, Notice of Compliance database, Notice of Compliance with conditions web site, Patented Medicine Prices Review Board, La revue Prescrire, World Health Organization (WHO) Anatomical Therapeutic Chemical (ATC) classification system. Interventions: None. Primary and secondary outcomes: Percent of new drugs evaluated by Health Canada that went through an accelerated pathway between 1995 and 2016 inclusive. Kappa values comparing the review status to assessments of therapeutic value for individual drugs. Results: 438 (70.3%) drugs went through the standard pathway, 185 (29.7%) an accelerated pathway. Therapeutic evaluations were available for 509 drugs. Health Canada used an accelerated approval pathway for 159 of the 509 drugs whereas only 55 were judged to be therapeutically innovative. The Kappa value for the entire period for all 509 drugs was 0.276 (95% CI 0.194, 0.359) or fair. Conclusion: Health Canada’s use of accelerated approvals was stable over the entire time period. Its ability to predict which drugs will offer a major therapeutic gain is relatively poor. The findings in this study should provoke a discussion about whether Health Canada should continue to use these pathways and if so how their use can be improved.
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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 | 0.028 |
| Meta-epidemiology (narrow) | 0.000 | 0.001 |
| Meta-epidemiology (broad) | 0.001 | 0.001 |
| Bibliometrics | 0.005 | 0.012 |
| Science and technology studies | 0.002 | 0.001 |
| Scholarly communication | 0.003 | 0.001 |
| Open science | 0.002 | 0.002 |
| Research integrity | 0.001 | 0.001 |
| 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".