The Allergic Rhinitis Clinical Investigator Collaborative – nasal allergen induced eosinophilia
Bibliographic record
Abstract
The Allergic Rhinitis Clinical Investigator Collaborative (AR-CIC) is a Canadian multi-center initiative with the primary goal of performing standardized nasal allergen challenge (NAC) to study the anti-allergic effects of novel therapeutic agents for allergic rhinitis (AR). The model further allows identification of potential mechanisms of allergic disease and biomarkers. In this study we examined differential counts, more specifically eosinophil numbers, in nasal lavage samples before, 1 hour (1H) and 6 hours (6H) after direct nasal allergen challenge. Thirty-three atopic and five non-atopic participants were enrolled at four study centers. All atopic participants had AR symptoms following exposure to environmental allergens and a supportive skin test response. Using the Pfeiffer Bidose Nasal Delivery Device 100μl allergen solution was delivered to each nostril. Atopic pilot study participants were challenged with a threshold dose of allergen determined via titration 1 week prior to NAC, non-atopic participants were challenged with a 1:2 allergen dose. The allergens used included either ragweed, grass, D. farina, D. pteronyssinus and cat hair. Nasal lavage samples were collected at baseline, 1H and 6H post NAC. Total cell counts (TCC) were determined on unstained samples prior to cytospin. Cytospin slides were prepared and differentially stained (i.e. DiffQuick). Atopic individuals exhibited eosinophilia at 1H and 6H post NAC when compared to baseline samples. Non-atopic participants did not display a significant increase in eosinophils at any time point. Furthermore, TCCs were increased at 1H post NAC in atopic participants. This trend was not observed in non-atopic samples. Differences were noted in eosinophil numbers (elevated) between baseline, 1H and 6H post direct NAC only in participants with AR. Nasal lavage collection for differential count analysis is a robust assay that can be integrated into clinical trials conducted using the AR-CIC.
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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 | 0.008 | 0.002 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.001 | 0.001 |
| Bibliometrics | 0.001 | 0.001 |
| Science and technology studies | 0.001 | 0.000 |
| Scholarly communication | 0.001 | 0.000 |
| Open science | 0.001 | 0.001 |
| Research integrity | 0.001 | 0.001 |
| Insufficient payload (model declined to judge) | 0.003 | 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".