Validation of a New Enzyme-Linked Immunosorbent Assay to Detect the Triggering Proteins and Peptides for Celiac Disease: Interlaboratory Study
Bibliographic record
Abstract
The performance of Gluten-Tec (EuroProxima, Arnhem, The Netherlands) was tested through an interlaboratory study in accordance with AOAC guidelines. Gluten-Tec is a competitive ELISA that detects an immunostimulatory epitope of a-gliadin in dietary food for celiacs. Fifteen laboratories, representing 14 different countries, announced their interest in taking part in this study. Of the 12 laboratories that sent the results within the established timeframe, two submitted inappropriate standard curves and were excluded from the statistical analysis. Four different food matrixes (rice-based baby food, maize bread, chocolate cake mix, and beer) were selected for preparing the test samples. Two gliadin extraction procedures were used: the conventional 60% ethanol, and a new method based on the reducing reagent dithiothreitol. The 38 samples (19 blind duplicates) tested in this study were prepared by diluting the different extracts in order to cover a wide range of gliadin levels. Both sample extraction and dilution were performed by EuroProxima; the present interlaboratory study was focused only on testing the ELISA part of the Gluten-Tec kit protocol. Repeatability values (within-laboratory variance), expressed as RSD(r) ranged from 6.2 to 25.7%, while reproducibility values (interlaboratory variance), expressed as RSD(R), ranged from 10.6 to 45.9%. Both statistical parameters were in the acceptable range of ELISAs under these conditions, and the method will be presented to the Codex Alimentarius as a preferred method for gluten analysis.
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How this classification was reachedexpand
Full frame machine prediction
Teacher imitationNot 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.
Distilled classifier scores by category (both heads)
| Category | Codex | Gemma |
|---|---|---|
| Metaresearch | 0.049 | 0.038 |
| Meta-epidemiology (narrow) | 0.004 | 0.002 |
| Meta-epidemiology (broad) | 0.002 | 0.002 |
| Bibliometrics | 0.003 | 0.003 |
| Science and technology studies | 0.002 | 0.003 |
| Scholarly communication | 0.003 | 0.001 |
| Open science | 0.003 | 0.003 |
| Research integrity | 0.004 | 0.002 |
| Insufficient payload (model declined to judge) | 0.001 | 0.002 |
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.
score_only:v0-immature-baseline · verbatim from the scoring run: score_only means the number may rank works, and no category label ships from itClassification
machine, unvalidatedMachine predicted; a candidate call from one source (direct Gemma or distilled Codex), not a consensus.
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".