OP100 How Health Technology Assessment Is Adapting To Orphan Drugs In Canada – Not!
Bibliographic record
Abstract
INTRODUCTION: Some countries have distinct pathways for drugs for rare diseases (DRDs) (1). In May 2014, the Canadian Agency for Technologies in Health (CADTH) rejected the option of a separate review pathway for DRDs, reiterating that “pharmacoeconomic analyses are critical for all types of drugs”. While the gap between positive recommendations for common and rare drugs may have narrowed, the rejection for DRDs is still proportionally much higher (2). The default has been to provincially negotiate drug access, for patient populations, subgroups or individuals. Still not wishing to create a separate pathway, in March 2016, CADTH produced a revised evaluation framework for “uncertain clinical and pharmacoeconomic evidence” and other considerations representing “significant unmet need” including rarity and difficulty to study because of small patient population”(3). This study analyzes recommendations for DRDs following the two CADTH revisions. METHODS: Methods used were: synthesis of previously conducted analyses of CADTH recommendations for rare and non-rare drugs, primary comparative analysis of CADTH recommendations for DRDs from 2004 to 2016, and qualitative analysis of two drugs submitted for both rare and non-rare conditions: everolimus (breast cancer, pancreatic neuroendocrine tumours, and tuberous sclerosis complex) and ibrutinib (chronic lymphocytic leukemia, small lymphocytic lymphoma, and Waldenström's Macroglobulinemia). RESULTS: Previous analyses found that DRDs received more negative recommendations than did non-rare drugs; both clinical and economic evidence were differentiating factors. The primary analysis provided an additional understanding of reasons for negative recommendations. There is low consistency across assessments and across the two CADTH review committees. The case studies illustrated the challenges for DRDs to overcome barriers of cost-effectiveness and certainty of clinical evidence, even with the revised framework. CONCLUSIONS: This research challenges the premise that Health Technology Assessment for all drugs can result in fair and equitable recommendations for DRDs. Moreover, assessments based on “significant unmet need” do not appear to provide consistent or equitable guidelines for addressing the issues specific to rare diseases.
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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 | 0.117 | 0.341 |
| Meta-epidemiology (narrow) | 0.001 | 0.001 |
| Meta-epidemiology (broad) | 0.002 | 0.003 |
| Bibliometrics | 0.010 | 0.020 |
| Science and technology studies | 0.010 | 0.008 |
| Scholarly communication | 0.022 | 0.009 |
| Open science | 0.005 | 0.007 |
| Research integrity | 0.007 | 0.008 |
| Insufficient payload (model declined to judge) | 0.030 | 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".