Basophil activation test compared to skin prick test and fluorescence enzyme immunoassay for aeroallergen-specific Immunoglobulin-E
Bibliographic record
Abstract
BACKGROUND: Skin prick test (SPT) and fluorescence enzyme immunoassay (FEIA) are widely used for the diagnosis of Immunoglobulin-E (IgE)-mediated allergic disease. Basophil activation test (BAT) could obviate disadvantages of SPT and FEIA. However, it is not known whether BAT gives similar results as SPT or FEIA for aeroallergens. OBJECTIVES: In this study, we compared the results of SPT, BAT and FEIA for different aeroallergens. METHODS: We performed BAT, SPT and FEIA in 41 atopic subjects (symptomatic and with positive SPT for at least 1 of 9 common aeroallergens) and 31 non-atopic subjects (asymptomatic and with negative SPT). RESULTS: Correlations between SPT and BAT, SPT and FEIA, and BAT and FEIA results were statistically significant but imperfect. Using SPT as the "gold standard", BAT and FEIA were similar in sensitivity. However, BAT had lower specificity than FEIA. False positive (BATposSPTneg) results were frequent in those atopic subjects who were allergic by SPT to a different allergen and rare in non-atopic subjects. The false positivity in atopic subjects was due in part to high levels of serum Total-IgE (T-IgE) levels in atopic individuals that lead to basophil activation upon staining with fluorochrome-labeled anti-IgE. CONCLUSION: As an alternative to SPT in persons allergic to aeroallergens, BAT in its present form is useful for distinguishing atopic from non-atopic persons. However, BAT in its present form is less specific than FEIA when determining the allergen which a patient is allergic to. This is due to IgE staining-induced activation of atopic person's basophils and/or nonspecific hyperreactivity of atopic person's basophils.
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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 | 0.001 | 0.003 |
| Meta-epidemiology (narrow) | 0.001 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.001 | 0.000 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.000 | 0.000 |
| Open science | 0.000 | 0.000 |
| Research integrity | 0.001 | 0.001 |
| Insufficient payload (model declined to judge) | 0.002 | 0.001 |
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".