VP21 Factors Associated With Recommendations On Drugs For Rare Diseases
Bibliographic record
Abstract
Introduction: In Canada, reimbursement recommendations on drugs for common and rare indications (for example, orphan drugs) are made through the pan-Canadian Oncology Drug Review (pCODR) and the Common Drug Review (CDR). However, some stakeholders have called for a separate mechanism for orphan drugs, arguing that existing processes place too much weight on their high price tags. The purpose of this study was to examine factors associated with positive recommendations on drugs for rare diseases. Methods: Information was extracted from CDR and pCODR recommendations on drugs for diseases (prevalence of less than 1 in 2,000) up to April 2018. Univariate and multivariate logistic regression models were applied to explore the influence of the following variables on recommendations: year; prevalence; clinical safety and effectiveness (safety, quality of life, symptoms, surrogate outcomes, and survival); quality of evidence (availability of comparative data, external validity, and bias); unmet need; treatment cost; and incremental cost-effective ratio (ICER). Two-way interactions were also tested. Results: Of 128 recommendations, fifty-four (77 percent) and forty (69 percent) were positive for cancer and non-cancer indications, respectively. For cancer indications, all submissions reporting meaningful improvements in surrogate, quality of life, and survival outcomes were significantly more likely to have a positive recommendation. Submissions showing a lack of external validity were significantly less likely to receive a positive recommendation. For non-cancer indications, more recent submissions and those presenting no safety issues were associated with positive recommendations. Prevalence, treatment cost, and ICER were not determinants of positive or negative recommendations. Conclusions: For both cancer and non-cancer orphan drugs, impact on clinical safety and effectiveness, rather than cost, appears to be a key factor in the formulation of recommendations.
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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 | 0.048 | 0.445 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.001 | 0.002 |
| Bibliometrics | 0.005 | 0.009 |
| Science and technology studies | 0.001 | 0.001 |
| Scholarly communication | 0.003 | 0.002 |
| Open science | 0.001 | 0.002 |
| Research integrity | 0.002 | 0.002 |
| Insufficient payload (model declined to judge) | 0.005 | 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".