Methodological guidelines and publications of benefit–risk assessment for health technology assessment: a scoping review
Bibliographic record
Abstract
OBJECTIVES: To map the available methodological guidelines and documents for conducting and reporting benefit-risk assessment (BRA) during health technologies' life cycle; and to identify methodological guidelines for BRA that could serve as the basis for the development of a BRA guideline for the context of health technology assessment (HTA) in Brazil. DESIGN: Scoping review. METHODS: Searches were conducted in three main sources up to March 2023: (1) electronic databases; (2) grey literature (48 HTA and regulatory organisations) and (3) manual search and contacting experts. We included methodological guidelines or publications presenting methods for conducting or reporting BRA of any type of health technologies in any context of the technology's life cycle. Selection process and data charting were conducted by independent reviewers. We provided a structured narrative synthesis of the findings. RESULTS: From the 83 eligible documents, six were produced in the HTA context, 30 in the regulatory and 35 involved guidance for BRA throughout the technology's life cycle. We identified 129 methodological approaches for BRA in the documents. The most commonly referred to descriptive frameworks were the Problem, Objectives, Alternatives, Consequences, Trade-offs, Uncertainty, Risk and Linked decisions and the Benefit-Risk Action Team. Multicriteria decision analysis was the most commonly cited quantitative framework. We also identified the most cited metric indices, estimation and utility survey techniques that could be used for BRA. CONCLUSIONS: Methods for BRA in HTA are less established. The findings of this review, however, will support and inform the elaboration of the Brazilian methodological guideline on BRA for HTA. TRIAL REGISTRATION NUMBER: https://doi.org/10.17605/OSF.IO/69T3V.
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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 | 0.274 | 0.621 |
| Meta-epidemiology (narrow) | 0.004 | 0.004 |
| Meta-epidemiology (broad) | 0.010 | 0.012 |
| Bibliometrics | 0.090 | 0.068 |
| Science and technology studies | 0.005 | 0.007 |
| Scholarly communication | 0.019 | 0.017 |
| Open science | 0.008 | 0.013 |
| Research integrity | 0.012 | 0.007 |
| Insufficient payload (model declined to judge) | 0.011 | 0.004 |
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".