A comparative analysis of international health technology assessments for novel gene silencing therapies: patisiran and inotersen
Bibliographic record
Abstract
Objectives: Using the case study of patisiran and inotersen, we conducted a narrative comparative analysis of the health technology assessment (HTA) agency appraisals of these two first-in-class transthyretin gene silencers, which represent exceptional advances in the treatment of hereditary transthyretin-mediated (hATTR) amyloidosis, a rare and multisystemic disease. Despite the impact of each product on the treatment landscape, the majority of HTAs are only considered standard of care as a comparator, resulting in a void of information and limited comprehension of the clinical and pharmacoeconomic differences between the two treatments. Methods: A search was conducted internationally for HTA reports, and only instances where assessment decisions for both treatments were publicly available were included in the present analysis. The HTA reports were analyzed broadly for the assessment of clinical and pharmacoeconomic evidence. Only economic models considering both patisiran and inotersen were included in this analysis. Results: A total of nine agencies with public assessment reports for both treatments were identified. HTA agency assessments for both treatments were essentially positive; however, differences were noted in the final recommendations, place in treatment or reimbursed indications, and in the narrative of the evaluations. Only the Canadian Agency for Drugs and Technologies in Health (CADTH) assessment for patisiran evaluated an economic model comparing the two treatments. Conclusions: The differences summarized in this comparative analysis may provide a more comprehensive overview of the two treatments.
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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 | 0.132 | 0.389 |
| Meta-epidemiology (narrow) | 0.001 | 0.001 |
| Meta-epidemiology (broad) | 0.002 | 0.007 |
| Bibliometrics | 0.011 | 0.015 |
| Science and technology studies | 0.001 | 0.002 |
| Scholarly communication | 0.008 | 0.005 |
| Open science | 0.002 | 0.004 |
| Research integrity | 0.003 | 0.003 |
| Insufficient payload (model declined to judge) | 0.009 | 0.000 |
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".