Reliance: a smarter way of regulating medical products - The IPRP survey
Bibliographic record
Abstract
Introduction: A survey was conducted among national regulatory authorities’ members of the International Pharmaceutical Regulators Programme (IPRP) to collect and share experiences of reliance approaches. Reliance allows formally, or informally, one regulatory authority to use assessments made by other regulatory authorities while remaining responsible for the final decision. Reliance is an essential concept to increase the efficiency of the global regulatory oversight of medical products by national regulatory authorities.Areas covered: This article describes the findings and recommendations from the IPRP survey. It shows that reliance in the area of medical product oversight is broadly accepted. The first part presents the acceptance and reasons for accepting reliance including the need for trust, then gives examples of the most common areas for reliance, and explains the difference between unilateral or reciprocal reliance. Finally, the article analyzes the lessons learned including challenges and opportunities for reliance on regulatory authorities to facilitate patient access in their jurisdictions.Expert opinion: Regulatory reliance facilitates regulatory approvals and allows to use resources in a more efficient way and ultimately serves patients by facilitating earlier access to quality-assured, safe, and effective medicines.
Fetched live from OpenAlex and de-inverted. Abstracts are not stored in this database: the inverted indexes are 8.6 GB of the frame’s 9.3 GB of text, and the host has 13 GB free.
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 | 0.036 | 0.084 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.002 | 0.002 |
| Science and technology studies | 0.001 | 0.003 |
| Scholarly communication | 0.004 | 0.006 |
| Open science | 0.001 | 0.004 |
| Research integrity | 0.002 | 0.005 |
| Insufficient payload (model declined to judge) | 0.004 | 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".