Can Standardisation of the Public Assessment Report Improve Benefit-Risk Communication?
Bibliographic record
Abstract
BACKGROUND: National regulatory authorities (NRAs) make the decision to register a medicine based on an assessment of its benefits and risks and publicly available assessment reports are used as a tool to communicate the basis for the decision. The Universal Methodology for Benefit-Risk Assessment (UMBRA) has also been used to effectively communicate the basis of regulatory decisions. Many NRAs in emerging markets place reliance on the public assessment reports (PARs) of reference agencies to inform about their own regulatory decisions. However, PAR users often criticise the redacted nature of PARs and may be challenged in identifying key benefits and risks, value judgements, and benefit-risk (BR) trade-offs. METHODS: PARs for ertugliflozin l-pyroglutamic acid, erenumab, and durvalumab published by regulatory bodies in Australia, Europe, Canada, and the United States were compared with the validated UMBRA Benefit-Risk Template to evaluate the BR decision documentation. Published validation of UMBRA included report of a consortium of four regulatory authorities in Australia, Canada, Switzerland, and Singapore indicating that their clinical assessment templates were modified to align with the UMBRA approach. A focus group discussed the use of PARs as potential knowledge management tools for stakeholder understanding of regulatory decision making. The South African Health Product Regulatory Authority (SAHPRA) approach to document and communicate the BR decisions was evaluated. RESULTS: Results indicate key elements to include in the PARs including regulatory history, an effects table and a record of the strengths and uncertainties for each benefit and risk. Focus group participants agreed that a harmonised PAR template would support improved regulatory decision-making transparency. SAHPRA communication of BR decisions could be improved through the use of the UMBRA BR Template as a guidance for BR assessment and the basis of the South Africa public assessment report format. CONCLUSION: SAHPRA's use of a structured template that supports transparent and quality decision making could have a major impact in ensuring consistency in the BR assessment of new medicines. The implementation of this effective approach for communicating BR decisions will advance agency goals of being a trusted, responsive, accountable regulatory body in which all healthcare stakeholders may rely on with confidence.
Fetched live from OpenAlex and de-inverted. Abstracts are not stored in this database: the inverted indexes are 8.6 GB of the frame’s 9.3 GB of text, and the host has 13 GB free.
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 | 0.639 | 0.806 |
| Meta-epidemiology (narrow) | 0.002 | 0.002 |
| Meta-epidemiology (broad) | 0.002 | 0.004 |
| Bibliometrics | 0.017 | 0.012 |
| Science and technology studies | 0.003 | 0.009 |
| Scholarly communication | 0.026 | 0.037 |
| Open science | 0.008 | 0.013 |
| Research integrity | 0.008 | 0.012 |
| Insufficient payload (model declined to judge) | 0.013 | 0.008 |
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".