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Record W2139470111 · doi:10.4155/bio.09.108

Incurred Sample Reproducibility: Views and Recommendations by the European Bioanalysis Forum

2009· article· en· W2139470111 on OpenAlexaff
Philip Timmerman, Silke Luedtke, Peter van Amsterdam, Margarete Brudny-Kloeppel, Berthold Lausecker, Stephanie Fischmann, Susanne Globig, Carl-Johan Sennbro, Josep M. Jansat, Hans Mulder, Elizabeth R. Thomas, Magnus Knutsson, Dirk Kasel, Stephen White, Morten A. Kall, Nathalie Mokrzycki-Issartel, Achim Freisleben, Fernando Alberto Álvarez Romero, Michael Andersen, Norbert Knebel, Marcel de Zwart, Sirpa Laakso, Richard Hucker, Dietmar Schmidt, Ben Gordon, Richard Abbott, Pierre Boulanger

Bibliographic record

VenueBioanalysis · 2009
Typearticle
Languageen
FieldImmunology and Microbiology
TopicBiosimilars and Bioanalytical Methods
Canadian institutionsAbbott (Canada)
Fundersnot available
KeywordsBioanalysisSample (material)SafeguardingReproducibilityComputer scienceProcess (computing)Data scienceMedicineChromatographyChemistry

Abstract

fetched live from OpenAlex

Following intensive discussions, review, alignment of procedures and multiple surveys among their member companies, the European Bioanalysis Forum (EBF) is providing a recommendation on how to integrate incurred sample reproducibility (ISR) in the bioanalytical process. The recommendation aims to provide guidance throughout the lifecycle of a validated method, including the application of the method in study support. In its recommendation, the EBF considers both the internal discussions with EBF member companies, as well as the input provided in international meetings where ISR was discussed. The ultimate goal of the EBF recommendation is to ensure that bioanalytical methods can provide accurate and reproducible concentration data for pharmacokinetic and/or toxicokinetic evaluation, without any compromise, while safeguarding the optimal use of laboratory resources.

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)0.250
metaresearch head score (Gemma)0.258
Version: metacan-v3-hybrid-931329e0061cValidation status: machine_predicted_unvalidated
Candidate categoriesMetaresearch
Consensus categoriesMetaresearch
DomainCandidate signal: Reproducibility · Consensus signal: none
Study designCandidate signal: Not applicable · Consensus signal: Not applicable
GenreCandidate signal: Methods · Consensus signal: none
Teacher disagreement score0.750
Threshold uncertainty score0.925

Distilled classifier scores by category (both heads)

CategoryCodexGemma
Metaresearch0.2500.258
Meta-epidemiology (narrow)0.0030.002
Meta-epidemiology (broad)0.0040.005
Bibliometrics0.0060.006
Science and technology studies0.0050.006
Scholarly communication0.0160.013
Open science0.0150.008
Research integrity0.0440.022
Insufficient payload (model declined to judge)0.0060.006

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 head0.038
GPT teacher head0.314
Teacher spread0.276 · 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 designNot applicable
DomainReproducibility
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

Citations90
Published2009
Admission routes1
Has abstractyes

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