Modeling the conversion between specific IgE test platforms for nut allergens in children and adolescents
Bibliographic record
Abstract
BACKGROUND: Multiplex tests allow for measurement of allergen-specific IgE responses to multiple extracts and molecular allergens and have several advantages for large cohort studies. Due to significant methodological differences, test systems are difficult to integrate in meta-analyses/systematic reviews since there is a lack of datasets with direct comparison. We aimed to create models for statistical integration of allergen-specific IgE to peanut/tree nut allergens from three IgE test platforms. METHODS: Plasma from Canadian and Austrian children/adolescents with peanut/tree nut sensitization and a cohort of sensitized, high-risk, pre-school asthmatics (total n = 166) were measured with three R&D multiplex IgE test platforms: Allergy Explorer version 1 (ALEX) (Macro Array Dx), MeDALL-chip (Mechanisms of Development of Allergy) (Thermo Fisher), and EUROLINE (EUROIMMUN). Skin prick test (n = 51) and ImmunoCAP (Thermo Fisher) (n = 62) results for extracts were available in a subset. Regression models (Multivariate Adaptive Regression Splines, local polynomial regression) were applied if >30% of samples were positive to the allergen. Intra-test correlations between PR-10 and nsLTP allergens were assessed. RESULTS: = 94%-56%) for peanut and tree nut sIgE testing at the extract and molecular-level, in order from highest to lowest: Ara h 2, Ara h 6, Jug r 1, Ana o 3, Ara h 1, Jug r 2, and Cor a 9. CONCLUSION: Our models support the notion that quantitative conversion is possible between sIgE multiplex platforms for extracts and molecular allergens and may provide options to aggregate data for future meta-analysis.
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How this classification was reachedexpand
Full frame distilled prediction
Teacher imitationNot 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.
Codex and Gemma teacher scores by category
| Category | Codex | Gemma |
|---|---|---|
| Metaresearch | 0.000 | 0.000 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.001 | 0.000 |
| Bibliometrics | 0.000 | 0.000 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.000 | 0.000 |
| Open science | 0.000 | 0.000 |
| Research integrity | 0.000 | 0.001 |
| Insufficient payload (model declined to judge) | 0.000 | 0.000 |
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.
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 teacher head, 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".