Health Technology Agency insights: informing modification of a qualitative benefit risk framework for Health Technology Reassessment of prescription medications
Bibliographic record
Abstract
OBJECTIVES: This study's intent was to determine if a qualitative benefit risk framework could be used or modified to further enable Health Technology Reassessment (HTR) of prescription medicine recommendations. The purpose of this research was to understand Canadian Health Technology Agency assessors past experiences and insights to inform any modifications to the Universal Methodology for Benefit-Risk Assessment (UMBRA) qualitative framework. The UMBRA framework consists of an eight-step process, used during the assessment phase, to aid in decision making and dissemination. METHODS: A qualitative descriptive study was conducted and included a purposeful, criterion-based sample of eight assessors who had participated in Health Technology Assessment (HTA) or HTR for prescription medicines or in qualitative decision-making frameworks. RESULTS: Participant interviews lead to four common themes: "adoption of a qualitative benefit risk framework," "data (either too much or not enough)," "importance of incorporating stakeholder values," and "feasibility of the UMBRA framework." Methodological challenges with HTR were highlighted including the lack of clinical outcome data and the ability to compare clinically relevant meaningful differences. The implementation of a ranking or weighing process found within the UMBRA framework was not favored by half of the participants. CONCLUSIONS: Research participants did not consider all steps of the UMBRA framework to be transferable to the assessment phase of HTR given the need for simplicity, resource efficiency, and stakeholder input throughout the process. The assessor experiences and insights and the resultant key themes can be used in future research to aid in the development of a qualitative recommendation framework for HTR.
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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 | 0.311 | 0.272 |
| Meta-epidemiology (narrow) | 0.002 | 0.002 |
| Meta-epidemiology (broad) | 0.001 | 0.002 |
| Bibliometrics | 0.006 | 0.004 |
| Science and technology studies | 0.015 | 0.025 |
| Scholarly communication | 0.013 | 0.013 |
| Open science | 0.005 | 0.018 |
| Research integrity | 0.004 | 0.006 |
| Insufficient payload (model declined to judge) | 0.006 | 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; the direct Gemma label and the distilled Codex classifier agree on what is shown here.
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".