The Brighton collaboration standardized module for vaccine benefit-risk assessment
Bibliographic record
Abstract
Vaccine Benefit-Risk (B-R) assessment consists of evaluating the benefits and risks of a vaccine and making a judgment whether the expected key benefits outweigh the potential key risks associated with its expected use. B-R supports regulatory and public health decision-making throughout the vaccine's lifecycle. In August 2021, the Brighton Collaboration's Benefit-Risk Assessment of VAccines by TechnolOgy (BRAVATO) Benefit-Risk Assessment Module working group was established to develop a standard module to support the planning, conduct and evaluation of structured B-R assessments for vaccines from different platforms, based on data from clinical trials, post-marketing studies and real-world evidence. It enables sharing of relevant information via value trees, effects tables and graphical depictions of B-R trade-offs. It is intended to support vaccine developers, funders, regulators and policy makers in high-, middle- or low-income countries to help inform decision-making and facilitate transparent communication concerning development, licensure, deployment and other lifecycle decisions.
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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 | 0.075 | 0.122 |
| Meta-epidemiology (narrow) | 0.002 | 0.001 |
| Meta-epidemiology (broad) | 0.002 | 0.003 |
| Bibliometrics | 0.007 | 0.004 |
| Science and technology studies | 0.001 | 0.001 |
| Scholarly communication | 0.004 | 0.005 |
| Open science | 0.003 | 0.006 |
| Research integrity | 0.003 | 0.004 |
| Insufficient payload (model declined to judge) | 0.061 | 0.033 |
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".