Change in exhaled nitric oxide during peanut challenge is related to severity of reaction
Bibliographic record
Abstract
Abstract Background Peanut allergy affects 3% of Australian children and has a higher risk of anaphylaxis than most food allergies. Predicting who is likely to develop anaphylaxis is still an inexact science. The fraction of exhaled nitric oxide (FeNO) shows promise as a biomarker involved in peanut allergy, as nitric oxide plays a role in inhibiting mast cell degranulation which is relevant in anaphylaxis, where mast cell degranulation plays a mediator role. The aim of this study was to assess the change in FeNO in children during peanut challenge. Methods Thirty-six children aged from 5 to 17 years were recruited for open-labelled peanut challenge. Participants had skin prick test to peanut performed, and serum collected for Ara h2 specific IgE and peanut specific IgE. FeNO was measured by portable device (NIOX VERO) prior to and throughout the peanut challenge. Results When grouped according to reaction type at peanut challenge (anaphylaxis, clinical allergy not anaphylaxis and tolerant), there were significant differences in the mean change in FeNO measurement between the anaphylaxis group and the clinical allergy, not anaphylaxis group (p = 0.005), and between the anaphylaxis group and tolerant group (p < 0.0001). Conclusions FeNO decreased more significantly in those who subsequently developed anaphylaxis than in those with clinical allergy, not anaphylaxis or negative peanut challenge (tolerance). As a bedside test that can be used in children, it has potential for further research into mechanisms of anaphylaxis in food allergy and potentially assists in predicting an imminent anaphylactic reaction in some patients. Trial registrationClinicalTrials.gov: PEAnut Anaphylaxis Predictors (PEAAP) NCT02424136.
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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 | 0.000 | 0.002 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 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) | 0.002 | 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 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".