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Record W3196393057 · doi:10.1016/j.ejps.2021.105987

The Global Bioequivalence Harmonisation Initiative (GBHI): Report of EUFEPS/AAPS fourth conference

2021· review· en· W3196393057 on OpenAlexaff
Blume Hh, Minesh P. Mehta, Gerald Beuerle, Angelica Dorantes, Georg Hempel, Wenlei Jiang, A. Kovar, Jieon Lee, Henrike Potthast, Barbara Schug, Anne Seidlitz, Nilufer Tampal, Y-C Tsang, J Walstab, Jan Welink

Bibliographic record

VenueEuropean Journal of Pharmaceutical Sciences · 2021
Typereview
Languageen
FieldMathematics
TopicStatistical Methods in Clinical Trials
Canadian institutionsApotex (Canada)
Fundersnot available
KeywordsBioequivalencePolitical sciencePharmaceutical industryEngineering ethicsManagement scienceLibrary scienceMedicineComputer sciencePharmacologyEngineeringBioavailability

Abstract

fetched live from OpenAlex

International Conference on Global Bioequivalence Harmonisation Initiative (GBHI) that was co-organised by the European Federation of Pharmaceutical Sciences (EUFEPS) and the American Association of Pharmaceutical Scientists (AAPS). The goal of the GBHI conference is to offer the most informative and up to date science and regulatory thinking of bioequivalence (BE) in global drug development to support the intended process of a scientific global harmonisation. The workshop provided an open forum for pharmaceutical scientists from academia, industry and regulatory agencies to discuss three BE topics of interest, (a) BE assessment for long-acting injectables and implants, (b) necessity of fed BE studies for immediate-release products and (c) procedures to demonstrate equivalence of orally inhaled products. Moreover, in keynote lectures, a potential road map to an international BE reference product was discussed, and visions and perspectives for future global BE harmonisation activities have been presented. The meeting delivered a cutting-edge insight into the topics in an interactive and at the same time focused way.

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)0.027
metaresearch head score (Gemma)0.015
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: Review
Teacher disagreement score0.027
Threshold uncertainty score0.144

Distilled classifier scores by category (both heads)

CategoryCodexGemma
Metaresearch0.0270.015
Meta-epidemiology (narrow)0.0010.001
Meta-epidemiology (broad)0.0020.002
Bibliometrics0.0040.005
Science and technology studies0.0010.001
Scholarly communication0.0040.003
Open science0.0020.002
Research integrity0.0030.005
Insufficient payload (model declined to judge)0.0050.002

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.

Opus teacher head0.890
GPT teacher head0.671
Teacher spread0.220 · 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

Citations12
Published2021
Admission routes1
Has abstractyes

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