The World Health Organization Global Benchmarking Tool an Instrument to Strengthen Medical Products Regulation and Promote Universal Health Coverage
Bibliographic record
Abstract
National regulatory authorities (NRAs) are the gatekeepers of the supply chain of medical products, and they have a mandate to ensure the quality, safety and efficacy of medicines, vaccines, blood, and blood products, medical devices, including diagnostics and traditional, or herbal medicines. However, the majority of the world's regulators are still struggling to reach a level of maturity, whereby they have a stable, well-functioning and integrated regulatory system. The World Health Organization (WHO) has developed a Global Benchmarking Tool (GBT) as part of its five-step capacity building program to assist NRAs, using the tool, they can benchmark their own strengths and areas of weakness, and then engage in a formal benchmarking process together with WHO and international experts in order to formulate an effective and workable institutional development plan. The GBT is comprehensive across the entire product life cycle and allows benchmarking to be customized to the needs of the NRA. It has evolved from decades of experience using a variety of benchmarking tools, within WHO and other stakeholder organizations. By the end of December 2019, 26 countries had undergone formal benchmarking, and a further 54 countries had used the GBT to conduct self-benchmarking exercises assisted by WHO.
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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 | 0.035 | 0.072 |
| Meta-epidemiology (narrow) | 0.001 | 0.000 |
| Meta-epidemiology (broad) | 0.001 | 0.001 |
| Bibliometrics | 0.014 | 0.020 |
| Science and technology studies | 0.001 | 0.001 |
| Scholarly communication | 0.004 | 0.006 |
| Open science | 0.001 | 0.006 |
| Research integrity | 0.001 | 0.002 |
| 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; 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".