Multi-Stakeholder Call to Action for the Future of Vaccine Post-Marketing Monitoring: Proceedings from the First Beyond COVID-19 Monitoring Excellence (BeCOME) Conference
Bibliographic record
Abstract
The response to the coronavirus disease 2019 (COVID-19) pandemic included a global effort to monitor the benefits and risks of vaccines and therapeutics, when used in the real world (i.e., ‘post-marketing’). This rapid and large-scale activity presented a new level of challenges, met with unprecedented levels of innovation, cooperation, and lay public communication. Similar challenges had been experienced in the 2009 H1N1 pandemic and, although some lessons learned triggered initiatives and solutions [ 1 , 2 ] to improve response and cooperation among key stakeholders, there was insufficient continuity and coordination of pandemic preparedness between all stakeholders following H1N1. To improve pandemic preparedness following COVID-19, it is critical to keep the current momentum of cooperation among key stakeholders and to expand the conversation to other vaccines and to therapeutics, when relevant.
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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 | 0.104 | 0.068 |
| Meta-epidemiology (narrow) | 0.001 | 0.001 |
| Meta-epidemiology (broad) | 0.001 | 0.002 |
| Bibliometrics | 0.002 | 0.001 |
| Science and technology studies | 0.015 | 0.008 |
| Scholarly communication | 0.022 | 0.016 |
| Open science | 0.004 | 0.020 |
| Research integrity | 0.029 | 0.043 |
| Insufficient payload (model declined to judge) | 0.015 | 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".