Why we need more collaboration in Europe to enhance post-marketing surveillance of vaccines
Bibliographic record
Abstract
The influenza A/H1N1 pandemic in 2009 taught us that the monitoring of vaccine benefits and risks in Europe had potential for improvement if different public and private stakeholders would collaborate better (public health institutes (PHIs), regulatory authorities, research institutes, vaccine manufacturers). The Innovative Medicines Initiative (IMI) subsequently issued a competitive call to establish a public-private partnership to build and test a novel system for monitoring vaccine benefits and risks in Europe. The ADVANCE project (Accelerated Development of Vaccine benefit-risk Collaboration in Europe) was created as a result. The objective of this paper is to describe the perspectives of key stakeholder groups of the ADVANCE consortium for vaccine benefit-risk monitoring and their views on how to build a European system addressing the needs and challenges of such monitoring. These perspectives and needs were assessed at the start of the ADVANCE project by the European Medicines Agency together with representatives of the main stakeholders in the field of vaccines within and outside the ADVANCE consortium (i.e. research institutes, public health institutes, medicines regulatory authorities, vaccine manufacturers, patient associations). Although all stakeholder representatives stated they conduct vaccine benefit-risk monitoring according to their own remit, needs and obligations, they are faced with similar challenges and needs for improved collaboration. A robust, rapid system yielding high-quality information on the benefits and risks of vaccines would therefore support their decision making. ADVANCE has developed such a system and has tested its performance in a series of proof of concept (POC) studies. The system, how it was used and the results from the POC studies are described in the papers in this supplementary issue.
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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 | 0.208 | 0.126 |
| Meta-epidemiology (narrow) | 0.001 | 0.001 |
| Meta-epidemiology (broad) | 0.001 | 0.003 |
| Bibliometrics | 0.002 | 0.002 |
| Science and technology studies | 0.005 | 0.006 |
| Scholarly communication | 0.020 | 0.034 |
| Open science | 0.004 | 0.023 |
| Research integrity | 0.014 | 0.010 |
| Insufficient payload (model declined to judge) | 0.013 | 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; 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".