Collaborative Real‐World Evidence Among Regulators: Lessons and Perspectives
Bibliographic record
Abstract
The International Coalition of Medicines Regulatory Authorities (ICMRA), comprising 38 global medicines regulatory authorities, collaborates on shared challenges, notably during the COVID-19 pandemic. This article focuses on the ICMRA COVID-19 Real-World Evidence (RWE) and Observational Studies Working Group. The Working Group aimed to address challenges related to RWE and observational studies during the pandemic, resulting in impactful studies and ICMRA statements on international collaboration for RWE and COVID-19 vaccine safety. Reflecting on 3 years of collaboration, the Working Group surveyed members for insights, and recommendations were formulated to enhance research preparedness, collaboration, and response to future public health emergencies. The lessons learned highlight the importance of global collaborations, governance structures for rapid decision-making, and effective utilization of existing networks. Recommendations include the establishment of an international governance structure, a "coalition of the willing" for swift research collaboration, dedicated sub-groups, periodic workshops, common protocols, joint timelines, and data model templates, leveraging existing infrastructure, and strengthening outreach for transparency and engagement. The Working Group envisions repurposing into an RWE strategic and operational entity, contributing to global public health emergency response mechanisms. In conclusion, the Working Group's success lies in effective communication, collaborative research, and leveraging existing infrastructure, with ongoing contributions to global emergency response mechanisms.
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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 | 0.434 | 0.447 |
| Meta-epidemiology (narrow) | 0.002 | 0.002 |
| Meta-epidemiology (broad) | 0.004 | 0.003 |
| Bibliometrics | 0.008 | 0.007 |
| Science and technology studies | 0.010 | 0.029 |
| Scholarly communication | 0.051 | 0.055 |
| Open science | 0.012 | 0.032 |
| Research integrity | 0.036 | 0.036 |
| Insufficient payload (model declined to judge) | 0.010 | 0.002 |
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".