Reproducibility of nasal allergen challenge in evaluating the efficacy of intranasal corticosteroid treatment
Bibliographic record
Abstract
BACKGROUND: Although nasal challenge with allergen has often been used to evaluate the efficacy of therapeutic modalities used for the treatment of allergic rhinitis, the reproducibility of this model in quantitatively evaluating efficacy has not been rigorously examined. OBJECTIVE: To examine the reproducibility of the suppressive effects of an intranasal corticosteroid on the clinical and biochemical outcomes of a nasal allergen challenge during two identical treatment periods using the same subjects. METHODS: In a single-blind study, 25 seasonal allergic subjects with positive skin tests to grass or ragweed were studied outside of their pollen season. Subjects underwent a baseline, three-dose allergen challenge. Beginning 1 week later, subjects received two 7-day courses of intranasal beclomethasone (168 microg b.i.d.) separated by a 1-month washout period. Nasal challenges with allergen were performed after each treatment period. The nasal allergic response was evaluated by counting sneezes, recording symptom scores and measuring levels of albumin (an index of vascular permeability), lysozyme (an index of serous glandular secretion) and kinins (proinflammatory peptides) in recovered nasal lavages. RESULTS: Compared with the baseline challenge, each course of beclomethasone significantly reduced sneezing, symptom scores, albumin and kinins, but not lysozyme. Reproducibility analysis of the net changes from diluent challenge in the two beclomethasone treatment periods, showed the following intraclass correlation coefficients: sneezing (0.92), lysozyme (0.82), symptom scores (0.72), albumin (0.64) and kinins (0.28). CONCLUSION: We conclude that the nasal challenge model is a reproducible method to evaluate the efficacy of anti-allergic medications. For nasal corticosteroid trials, sneezing, symptom scores and albumin levels are recommended as the most reproducibly suppressive outcome measures.
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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 | 0.002 | 0.001 |
| 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 | 0.000 |
| 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".