Nasal challenges in allergen immunotherapy trials
Bibliographic record
Abstract
PURPOSE OF REVIEW: The nasal allergen challenge (NAC) model can be a valuable diagnostic tool for allergic rhinitis. Alongside its clinical use, NACs can be used as primary and secondary endpoints in studies evaluating allergen immunotherapy (AIT) products for allergic rhinitis treatment. This review will discuss the technical aspects of the NAC model and provide a summary of recent studies using NACs to assess existing and new AIT treatments. RECENT FINDINGS: Over the last 2 years, both titrated and single-dose nasal challenge protocols have been used to evaluate immunotherapies targeting grass, birch, house dust mite, and cat allergens. Early efficacy and dose-finding trials showed improvements in allergic symptoms and nasal tolerance to allergens after AIT treatment with standardized extracts or modified forms of whole allergen. NACs were also used in two proof-of-concept studies to illustrate the efficacy of intralymphatic immunotherapy with two concomitant allergens and subcutaneous immunotherapy with Fel d 1-specific IgG-blocking antibodies. SUMMARY: Along with existing therapies, nasal challenges are useful in evaluating AIT treatments in the very early stages of clinical development. However, because of the variety in challenge techniques and symptom assessments available, special attention must be placed in the protocol design in order to compare the study results with existing NAC publications.
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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 | 0.006 | 0.015 |
| Meta-epidemiology (narrow) | 0.001 | 0.000 |
| Meta-epidemiology (broad) | 0.004 | 0.002 |
| Bibliometrics | 0.002 | 0.002 |
| Science and technology studies | 0.000 | 0.001 |
| Scholarly communication | 0.002 | 0.001 |
| Open science | 0.002 | 0.001 |
| Research integrity | 0.003 | 0.002 |
| Insufficient payload (model declined to judge) | 0.011 | 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".