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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 distilled prediction

Teacher imitation

Not 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.

metaresearch head score (Codex)0.035
metaresearch head score (Gemma)0.131
Version: codex-gemma-dda1882f352aValidation status: machine_predicted_unvalidated
Candidate categoriesMetaresearch, Science and technology studies
Consensus categoriesMetaresearch
DomainCandidate signal: none · Consensus signal: none
Study designCandidate signal: Other design · Consensus signal: none
GenreCandidate signal: Review · Consensus signal: Review
Teacher disagreement score0.930
Threshold uncertainty score1.000

Codex and Gemma teacher scores by category

CategoryCodexGemma
Metaresearch0.0350.131
Meta-epidemiology (narrow)0.0000.000
Meta-epidemiology (broad)0.0020.001
Bibliometrics0.0000.001
Science and technology studies0.0000.003
Scholarly communication0.0000.000
Open science0.0020.000
Research integrity0.0000.001
Insufficient payload (model declined to judge)0.0000.000

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; both teacher heads agree on what is shown here.

Study designOther design
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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