OP67 Considerations Of Treatment Novelty In Health Technology Assessment
Bibliographic record
Abstract
Introduction A recent proliferation of value frameworks, as well as the emergence of innovative approaches to treating disease (e.g., cell/gene therapies) have been accompanied by an increased focus on nontraditional elements of value. We sought to understand whether and how health technology assessment (HTA) agencies consider novel aspects of treatment in value assessments. Methods We defined treatment novelty as follows: (i) a new mechanism of action or administration; (ii) addresses an unmet need; or (iii) confers a distinct benefit that transforms clinical practice or that is difficult to quantify. We reviewed technical guidance and peer-reviewed literature to investigate how organizations in eight countries (Australia, Canada, England, France, Norway, the Netherlands, Sweden, and the United States) consider aspects of this definition. Results All (n = 8) organizations give special consideration to interventions that address an unmet need, particularly in cancer, rare diseases, and other severe conditions. Nearly all (n = 5) organizations consider whether an intervention produces benefits that may not be adequately quantified. Organizations in England, Norway, and France sometimes recommend drugs with less favorable cost-effectiveness estimates than traditionally considered if the drug addresses rare or severe conditions, or if its quality-of-life benefit is thought to be inadequately quantified. The Institute for Clinical and Economic Review in the United States models cost-effectiveness in rare diseases using both a modified societal and health care system perspective. Importantly, the benefits of novel treatments are frequently considered uncertain, particularly treatments with a new mechanism of action. When uncertainty is high, organizations in Canada, England, France, the Netherlands, and Sweden sometimes issue conditional recommendations until additional evidence is submitted. England and Australia have used risk sharing agreements for drugs determined to be novel but uncertain. Conclusions The most widely considered aspects of treatment novelty in HTA are unmet needs and potential benefits that are not easily measured. The willingness to pay for novel treatments is often greater, despite inherent uncertainties about benefit and cost-effectiveness.
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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 | 0.326 | 0.571 |
| Meta-epidemiology (narrow) | 0.001 | 0.001 |
| Meta-epidemiology (broad) | 0.002 | 0.004 |
| Bibliometrics | 0.009 | 0.010 |
| Science and technology studies | 0.003 | 0.014 |
| Scholarly communication | 0.019 | 0.016 |
| Open science | 0.003 | 0.008 |
| Research integrity | 0.008 | 0.010 |
| Insufficient payload (model declined to judge) | 0.008 | 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".