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Record W3112986797 · doi:10.1007/s40264-020-01008-0

Effective Pharmacovigilance System Development: EFPIA-IPVG Consensus Recommendations

2020· review· en· W3112986797 on OpenAlexaff
Tanja Peters, Nigel Soanes, Maya Abbas, Jabeen Ahmad, Jean-Christophe Delumeau, Esteban Herrero‐Martínez, Mélanie Paramananda, Johanna Piper, Fairouz Smail-Aoudia, Willemijn van der Spuij, Tina Veizovic, Gillian Winstanley

Bibliographic record

VenueDrug Safety · 2020
Typereview
Languageen
FieldPharmacology, Toxicology and Pharmaceutics
TopicPharmacovigilance and Adverse Drug Reactions
Canadian institutionsAmgen (Canada)
Fundersnot available
KeywordsPharmacovigilanceLegislationMedicineScope (computer science)BenchmarkingEuropean unionObligationBusinessMarketing authorizationRisk analysis (engineering)PharmacologyMarketingInternational tradePolitical science

Abstract

fetched live from OpenAlex

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.

How this classification was reachedexpand

Full frame machine prediction

Teacher imitation

Not 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.

metaresearch head score (Codex)0.173
metaresearch head score (Gemma)0.172
Version: metacan-v3-hybrid-931329e0061cValidation status: machine_predicted_unvalidated
Candidate categoriesnone
Consensus categoriesnone
DomainCandidate signal: none · Consensus signal: none
Study designCandidate signal: Not applicable · Consensus signal: none
GenreCandidate signal: Review · Consensus signal: none
Teacher disagreement score0.173
Threshold uncertainty score0.917

Distilled classifier scores by category (both heads)

CategoryCodexGemma
Metaresearch0.1730.172
Meta-epidemiology (narrow)0.0030.002
Meta-epidemiology (broad)0.0040.010
Bibliometrics0.0100.007
Science and technology studies0.0030.005
Scholarly communication0.0140.013
Open science0.0160.013
Research integrity0.0290.020
Insufficient payload (model declined to judge)0.0160.013

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.

Opus teacher head0.135
GPT teacher head0.476
Teacher spread0.341 · how far apart the two teachers sit on this one work
Validation statusscore_only:v0-immature-baseline · verbatim from the scoring run: score_only means the number may rank works, and no category label ships from it

Classification

machine, unvalidated

Machine predicted; a candidate call from one source (direct Gemma or distilled Codex), not a consensus.

The models applied no category: nothing in the taxonomy fit this work.
Study designNot applicable
Domainnot available
GenreReview

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".

Quick stats

Citations23
Published2020
Admission routes1
Has abstractyes

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