Roles and Rules of Some Regulatory Agencies around the World during COVID-19 Pandemic
Bibliographic record
Abstract
Objective: Collecting and synthesizing relevant data on COVID-19 from official sources of some different regulatory agencies around the world. Methods: The information and actions related to responding to the COVID-19 situation were collected from the websites of some regulatory agencies, including the US Food and Drug Administration (FDA), the European Medicines Agency (EMA), Health Canada (HC), Swiss Agency for Therapeutic Products (Swissmedic), and the Australian Therapeutic Goods Administration (TGA). Results: All the regulatory agencies help in expediting the development of COVID-19 treatments and medical devices. These agencies also developed an international regulatory collaboration to develop cure models for the pandemic. While some of the agencies conduct the COVID-19 testing, like the US FDA, the others do not. The agencies also differ in their approaches towards resolving the pandemic. FDA and EMA are more aggressive in a way that they prioritize more testing and hospitalization coverage. However, as of the 22nd of June 2021, the FDA authorized the highest number (388) of diagnostic COVID-19 test kits followed by TGA (128), and EMA (88). Conclusions: Although the regulatory agencies differ in their approaches towards resolving pandemic COVID-19, all regulatory agencies help in expediting the development of COVID-19 treatments and medical devices.
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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 | 0.107 | 0.167 |
| Meta-epidemiology (narrow) | 0.001 | 0.001 |
| Meta-epidemiology (broad) | 0.001 | 0.001 |
| Bibliometrics | 0.010 | 0.011 |
| Science and technology studies | 0.005 | 0.005 |
| Scholarly communication | 0.013 | 0.008 |
| Open science | 0.002 | 0.004 |
| Research integrity | 0.004 | 0.004 |
| Insufficient payload (model declined to judge) | 0.005 | 0.001 |
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".