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Record W4406489663 · doi:10.1111/bcp.16393

Optimizing real‐world evidence studies for regulatory decision‐making and impact assessment in pharmacovigilance

2025· article· en· W4406489663 on OpenAlexaboutno aff
Ignatios Ioakeim‐Skoufa, Kerry Atkins, Miguel Ángel Hernández Rodríguez

Bibliographic record

VenueBritish Journal of Clinical Pharmacology · 2025
Typearticle
Languageen
FieldPharmacology, Toxicology and Pharmaceutics
TopicPharmacovigilance and Adverse Drug Reactions
Canadian institutionsnot available
Fundersnot available
KeywordsPharmacovigilanceReal world evidenceMedicinePharmacologyRisk analysis (engineering)DrugInternal medicine

Abstract

fetched live from OpenAlex

This manuscript is an original study, has not been published elsewhere, nor prior postings/presentations, and is not currently under consideration by another journal. All authors contributed significantly to this work and have read and approved the final version submitted.

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)0.596
metaresearch head score (Gemma)0.779
Version: metacan-v3-hybrid-931329e0061cValidation status: machine_predicted_unvalidated
Candidate categoriesMetaresearch
Consensus categoriesMetaresearch
DomainCandidate signal: Methods · Consensus signal: Methods
Study designCandidate signal: Theoretical or conceptual · Consensus signal: none
GenreCandidate signal: Methods · Consensus signal: Methods
Teacher disagreement score0.404
Threshold uncertainty score0.499

Distilled classifier scores by category (both heads)

CategoryCodexGemma
Metaresearch0.5960.779
Meta-epidemiology (narrow)0.0050.003
Meta-epidemiology (broad)0.0130.016
Bibliometrics0.0160.011
Science and technology studies0.0020.005
Scholarly communication0.0280.016
Open science0.0100.013
Research integrity0.0110.012
Insufficient payload (model declined to judge)0.0290.005

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 head0.305
GPT teacher head0.647
Teacher spread0.343 · 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; the direct Gemma label and the distilled Codex classifier agree on what is shown here.

Study designTheoretical or conceptual
DomainMethods
GenreMethods

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

Citations4
Published2025
Admission routes1
Has abstractyes

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