Should patients with mild asthma be excluded from allergic rhinitis drug trials?
Bibliographic record
Abstract
Introduction: The comorbidity of allergic rhinitis (AR) and asthma has been recognized as an important consideration in successful asthma treatment and control. In the study of allergy treatments, often patients with allergic rhinitis with asthma are excluded from the study. Objectives: To examine whether patients with allergic rhinitis and asthma (ARA) respond differently than their AR alone counterparts to antihistamine treatment in a controlled natural allergen challenge of an EEC. Methods: 24 patients with positive SPT and history to house dust mite (HDM) were consented and studied to controlled HDM exposure in an EEC. 4/24 patients had mild asthma (GINA I). Patients were studied at baseline for 3 hrs and if they achieved 6/12 on nasal symptoms there were randomized to one of two treatment arms: active, cetirizine HCl or placebo, and studied for an additional 8h in the EEC. Patients recorded their nasal symptoms every 15 minutes over the first 2 hrs, and every half-hour for the remainder. The AUC for active vs placebo treatment was compared using a student9s t-test. Results: When AR+ARA data was included, cetirizine showed a 34.6% reduction in symptoms compared to placebo. This did not reach significance. When ARA were excluded, there was a 46.8% efficacy (p<0.05).
Fetched live from OpenAlex and de-inverted. Abstracts are not stored in this database: the inverted indexes are 8.6 GB of the frame’s 9.3 GB of text, and the host has 13 GB free.
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 | 0.049 | 0.051 |
| Meta-epidemiology (narrow) | 0.001 | 0.000 |
| Meta-epidemiology (broad) | 0.004 | 0.002 |
| Bibliometrics | 0.001 | 0.001 |
| Science and technology studies | 0.001 | 0.001 |
| Scholarly communication | 0.003 | 0.003 |
| Open science | 0.001 | 0.001 |
| Research integrity | 0.009 | 0.004 |
| Insufficient payload (model declined to judge) | 0.006 | 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".