Reasons for prescribing second generation antihistamines to treat allergic rhinitis in real-life conditions and patient response
Bibliographic record
Abstract
BACKGROUND: Second generation H1 antihistamines (H1A) are currently recommended as first choice medications for allergic rhinitis and rhinoconjunctivitis. However, little is known about what influences the choice of prescription of one second generation (H1A) as opposed to another in real-life conditions. OBJECTIVE: The aim of the study was to identify the main criteria determining the choice of a second generation H1A by allergy specialists in mainland France. METHODS: Consecutive patients suffering from allergic rhinitis or rhinoconjunctivitis were included and followed prospectively for 30 days from the prescription of a second generation H1A in monotherapy. Patients were asked to fill in auto-questionnaires at baseline, daily during the first 10 days of the new treatment, and at the end of follow-up. Data on efficacy, tolerance, safety, rate and type of response to treatment, as well as patient satisfaction were recorded and analyzed. RESULTS: 1,080 patients were included between March 2011 and October 2012, mostly suffering from moderate to severe rhinitis (82.0%). The most frequently cited reason for choosing a specific H1A was the expected efficacy (85.3%). The mean time to nasal and ocular recovery was 6 days and 78.2% of patients responded to treatment within this interval. The presence of conjunctivitis was significantly associated with a more rapid response. At the end of follow-up, the satisfaction rate was higher for patients who were switched from a previous treatment (87.5%), compared to those receiving their first treatment (78.8%). CONCLUSION AND CLINICAL RELEVANCE: The main reason for choosing a specific second generation H1A was its expected efficacy. Concomitant conjunctivitis is associated with a more rapid response to treatment. Symptom recovery necessitates a mean of 6 days.
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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 | 0.002 | 0.010 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.001 |
| 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 | 0.001 | 0.001 |
| Insufficient payload (model declined to judge) | 0.002 | 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 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".