Influencing Factors of Health Technology Assessment to Orphan Drugs: Empirical Evidence in England, Scotland, Canada, and Australia
Bibliographic record
Abstract
This study summarizes the intrinsic criteria for the recommendation of orphan drugs in England, Scotland, Canada, and Australia with the aim of understanding the rationale for the variability in decision-making and to provide a reference for the establishment of criteria in the process of access to health insurance for orphan drugs in different countries and the construction of national uniform criteria. A comparative analysis of 60 health technology assessment (HTA) guidelines of 15 drug-indication pairs appraised by four countries (England, Scotland, Canada, and Australia) from 2017 to 2018 was done, including an in-depth analysis of a case study. Agreement levels were measured using kappa scores. Associations were explored through correspondence analysis. The four countries possess some homogeneity in the assessment, but each has its own preferences. Poor agreement exists between England, Scotland, and Canada (-0.41 < kappa score < 0.192). In the correspondence analysis, England placed more emphasis on treatment methods in terms of control type when making recommendations. Canada and Scotland focused more on trial type with Canada placing more emphasis on phase III and open-label trials and on cost-utility analysis, while Australia was less studied in terms of economic models. Different countries have different goals when establishing HTA decisions for orphan drugs due to their different degrees of orphan drug coverage. Different countries should not only combine their unique values of clinical benefit and cost-effectiveness in the assessment of orphan drugs but also give different weights during the HTA process, after considering account the development of the country itself.
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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 | 0.037 | 0.216 |
| Meta-epidemiology (narrow) | 0.000 | 0.001 |
| Meta-epidemiology (broad) | 0.001 | 0.002 |
| Bibliometrics | 0.006 | 0.012 |
| Science and technology studies | 0.002 | 0.003 |
| Scholarly communication | 0.005 | 0.002 |
| Open science | 0.002 | 0.003 |
| Research integrity | 0.001 | 0.002 |
| Insufficient payload (model declined to judge) | 0.002 | 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".