EAACI position paper on the clinical use of the bronchial allergen challenge: Unmet needs and research priorities
Bibliographic record
Abstract
Allergic asthma (AA) is a common asthma phenotype, and its diagnosis requires both the demonstration of IgE-sensitization to aeroallergens and the causative role of this sensitization as a major driver of asthma symptoms. Therefore, a bronchial allergen challenge (BAC) would be occasionally required to identify AA patients among atopic asthmatics. Nevertheless, BAC is usually considered a research tool only, with existing protocols being tailored to mild asthmatics and research needs (eg long washout period for inhaled corticosteroids). Consequently, existing BAC protocols are not designed to be performed in moderate-to-severe asthmatics or in clinical practice. The correct diagnosis of AA might help select patients for immunomodulatory therapies. Allergen sublingual immunotherapy is now registered and recommended for controlled or partially controlled patients with house dust mite-driven AA and with FEV1 ≥ 70%. Allergen avoidance is costly and difficult to implement for the management of AA, so the proper selection of patients is also beneficial. In this position paper, the EAACI Task Force proposes a methodology for clinical BAC that would need to be validated in future studies. The clinical implementation of BAC could ultimately translate into a better phenotyping of asthmatics in real life, and into a more accurate selection of patients for long-term and costly management pathways.
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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 | 0.079 | 0.083 |
| Meta-epidemiology (narrow) | 0.002 | 0.001 |
| Meta-epidemiology (broad) | 0.004 | 0.003 |
| Bibliometrics | 0.003 | 0.003 |
| Science and technology studies | 0.003 | 0.003 |
| Scholarly communication | 0.012 | 0.011 |
| Open science | 0.006 | 0.006 |
| Research integrity | 0.025 | 0.018 |
| Insufficient payload (model declined to judge) | 0.024 | 0.017 |
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".