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A multi-laboratory evaluation of a clinically-validated incurred quality control material for analysis of allergens in food

2013· article· en· W2040609923 on OpenAlexaff
Phil Johnson, Neil M. Rigby, J. Dainty, Alan R. Mackie, Ulrike Immer, Adrian Rogers, Pauline Titchener, Masahiro Shoji, Anne Ryan, Luis Mata, Helen Brown, Thomas Holzhauser, Valéry Dumont, Jill A. Wykes, Michael Walker, Jon Griffin, Jane F. White, Glenn Taylor, Bert Pöpping, René Crevel, Petra Lutter, Ferdelie E. Gaskin, Terry Koerner, Dean Clarke, Robin Sherlock, Andrew Flanagan, Chun‐Han Chan, E. N. Clare Mills

Bibliographic record

VenueFood Chemistry · 2013
Typearticle
Languageen
FieldMedicine
TopicFood Allergy and Anaphylaxis Research
Canadian institutionsHealth Canada
FundersBiotechnology and Biological Sciences Research CouncilFood Standards Agency
KeywordsAllergenCaseinFood scienceSkimmed milkEgg whiteImmunoassayMilk proteinWhole milkAllergyChemistryMedicineImmunologyAntibody

Abstract

fetched live from OpenAlex

A dessert matrix previously used for diagnosis of food allergies was incurred with pasteurised egg white or skimmed milk powder at 3, 6, 15 and 30 mg allergen protein per kg of dessert matrix and evaluated as a quality control material for allergen analysis in a multi-laboratory trial. Analysis was performed by immunoassay using five kits each for egg and milk (based on casein) and six 'other' milk kits (five based on β-lactoglobulin and one total milk). All kits detected allergen protein at the 3 mg kg(-1) level. Based on ISO criteria only one egg kit accurately determined egg protein at 3 mg kg(-1) (p=0.62) and one milk (casein) kit accurately determined milk at 6 (p=0.54) and 15 mg kg(-1) (p=0.83), against the target value. The milk "other" kits performed least well of all the kits assessed, giving the least precise analyses. The incurred dessert material had the characteristics required for a quality control material for allergen analysis.

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)0.002
metaresearch head score (Gemma)0.002
Version: codex-gemma-dda1882f352aValidation status: machine_predicted_unvalidated
Candidate categoriesInsufficient payload (model declined to judge)
Consensus categoriesnone
DomainCandidate signal: none · Consensus signal: none
Study designCandidate signal: Bench or experimental · Consensus signal: Bench or experimental
GenreCandidate signal: Empirical · Consensus signal: Empirical
Teacher disagreement score0.087
Threshold uncertainty score1.000

Codex and Gemma teacher scores by category

CategoryCodexGemma
Metaresearch0.0020.002
Meta-epidemiology (narrow)0.0000.000
Meta-epidemiology (broad)0.0010.000
Bibliometrics0.0000.001
Science and technology studies0.0000.000
Scholarly communication0.0000.000
Open science0.0000.000
Research integrity0.0000.000
Insufficient payload (model declined to judge)0.0010.000

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 head0.095
GPT teacher head0.409
Teacher spread0.314 · 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 teacher head, not a consensus.

Study designBench or experimental
Domainnot available
GenreEmpirical

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

Citations45
Published2013
Admission routes1
Has abstractyes

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