Effective Pharmacovigilance System Development: EFPIA-IPVG Consensus Recommendations
Why this work is in the frame
A frame that forgets how it found something cannot be audited. These are the routes that admitted this work.
Bibliographic record
Abstract
Pharmaceutical legislation provides a legal framework to ensure the safe and effective use of medicines. This framework requires national regulatory authorities (NRAs) to establish and maintain a pharmacovigilance system (PV system) stating and enforcing the regulatory commitments that key stakeholders, including marketing authorisation holders (MAHs), are required to fulfil. In recent years, national legislative bodies and NRAs across the world have issued a significant amount of legislation and guidance enforcing the obligation to perform pharmacovigilance activities. In countries where the NRA is a member of the International Council for Harmonisation of Technical Requirements for Pharmaceuticals for Human Use (ICH), safety management requirements are generally consistent with ICH guidelines. In a number of countries beyond this scope, requirements may deviate from internationally agreed standards, adding a substantial complexity and increasing burden on the stakeholders involved, whilst the benefit for patients' safety may not be evident. Committed to fulfilling safety-regulatory obligations in any country where a product licence is held, global pharmaceutical companies have accumulated a broad and deep experience acquired whilst meeting the expectations of a large array of diverse PV systems across the world. These range from sub-optimal frameworks, according to the World Health Organization (WHO) Global Benchmarking Tool, to highly effective resource-optimised PV systems. In order to support countries creating or further developing their PV systems, especially where infrastructure and resources are limited, the European Federation of Pharmaceutical Industries and Associations (EFPIA) International Pharmacovigilance Group (IPVG) has developed consensus recommendations consistent with harmonised standards for the development and step-wise implementation of key PV system components. These recommendations endorsed by the EFPIA membership constitute the focus of this review article.
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.
Full frame distilled prediction
Teacher imitationNot calibrated prevalence, not ground truth. Human validation pending. Learned from the 10,348 direct Codex labels and 10,348 direct Gemma labels. Candidate is the union of thresholded teacher heads; consensus is their intersection. These outputs are machine_predicted_unvalidated and are not human labels or direct frontier model labels.
Codex and Gemma teacher scores by category
| Category | Codex | Gemma |
|---|---|---|
| Metaresearch | 0.001 | 0.000 |
| Meta-epidemiology (narrow) | 0.001 | 0.001 |
| Meta-epidemiology (broad) | 0.003 | 0.001 |
| Bibliometrics | 0.000 | 0.001 |
| Science and technology studies | 0.001 | 0.000 |
| Scholarly communication | 0.000 | 0.000 |
| Open science | 0.001 | 0.000 |
| Research integrity | 0.001 | 0.004 |
| Insufficient payload (model declined to judge) | 0.001 | 0.007 |
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 it