Bibliographic record
Abstract
Introduction Health technology assessment (HTA) bodies evaluate the clinical and/or economic impact of new therapies to inform public reimbursement decision-making. This research evaluates the value for money of current or proposed fees for HTA in countries with mandatory cost-effectiveness HTA bodies relative to their respective public drug expenditure. Methods HTA appraisal fees were identified from publicly-available websites: National Institute for Health and Care Excellence (NICE), Canadian Agency for Drugs and Technologies in Health (CADTH), Institut National d'Excellence en Santé et Services Sociaux (INESSS), and Pharmaceutical Benefits Advisory Committee (PBAC). Annual national public drug expenditure (ANPDE) were sourced from the National Health Service England, Canadian Institute for Health Information, and the Pharmaceutical Benefit Scheme. Results NICE is proposing to charge GBP 126,000 (EUR 142,582) for a single technology or highly specialized technology appraisal, CADTH charges CAD 72,480 (EUR 48,576) for a Schedule A submission, INESSS charges CAD 38,921 (EUR 26,089) for the first evaluation of a new drug or new indication, and PBAC charges AUD 136,716 (EUR 87,576) for a Major Lodgment. The ANPDE in England: GBP 16 billion (EUR 18.1 billion), Canada: CAD 14.5 billion (EUR 9.7 billion), Quebec: CAD 4 billion (EUR 2.7 billion) and Australia: AUD 8.7 billion (EUR 5.6 billion). The appraisal cost to drug expenditure ratio for these countries/regions were: 126,984, 200,055, 102,772, and 63,636, respectively. Conclusions HTA submissions in the United Kingdom, Canada and Australia require financial contributions from manufacturers. These contributions bear little relation to the market size and cumulatively exceed EUR 300,000 (assuming no resubmissions). By adopting charging/cost recovery models, HTA bodies are aiming to reinvest the proceeds to increase the efficiency and capacity of appraisals, expediting patient access. However, these fees may be burdensome, especially for SMEs with promising therapies for orphan/rare diseases, and they may thus have the potential to deter/delay their submissions.
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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 | 0.008 | 0.077 |
| Meta-epidemiology (narrow) | 0.001 | 0.000 |
| Meta-epidemiology (broad) | 0.001 | 0.002 |
| Bibliometrics | 0.003 | 0.004 |
| Science and technology studies | 0.001 | 0.001 |
| Scholarly communication | 0.009 | 0.002 |
| Open science | 0.001 | 0.002 |
| Research integrity | 0.002 | 0.002 |
| Insufficient payload (model declined to judge) | 0.083 | 0.011 |
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".