Reliance is key to effective access and oversight of medical products in case of public health emergencies
Bibliographic record
Abstract
INTRODUCTION: Responding to new threats and public health emergencies (PHE) creates serious challenges to regulators. The pandemic due to SARS-CoV-2 has been the catalyzer for change in global and local regulatory practices. Intensified collaboration, rapid and coordinated actions, and reliance mechanisms were key elements of the regulators' response to COVID-19 for all regulatory functions. AREAS COVERED: This article presents how collaboration and reliance among regulators were crucial tools for the regulatory responses to COVID-19, describes the reliance approaches for authorization of COVID-19 vaccines and other commodities, and the importance of reliance for other regulatory functions to avoid duplication and save resources where possible. This article also presents the results of a follow-up survey of reliance approaches in case of public health emergencies conducted between the International Pharmaceutical Regulators Programme (IPRP) members and discusses the forward-looking potential of reliance, analyzing the journey from theoretical concepts to real-life implementation. EXPERT OPINION: Regulatory reliance is an essential tool for regulators to act quickly and collectively in times of public health emergencies. Reliance approaches facilitate regulatory approvals and allow a more efficient use of resources, ultimately serving patients by facilitating earlier access to quality assured, safe and effective medicines.
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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 | 0.062 | 0.113 |
| Meta-epidemiology (narrow) | 0.000 | 0.001 |
| Meta-epidemiology (broad) | 0.001 | 0.001 |
| Bibliometrics | 0.002 | 0.002 |
| Science and technology studies | 0.002 | 0.008 |
| Scholarly communication | 0.010 | 0.008 |
| Open science | 0.003 | 0.006 |
| Research integrity | 0.006 | 0.008 |
| Insufficient payload (model declined to judge) | 0.010 | 0.003 |
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".