Impact of Allergic Rhinitis Symptoms on Quality of Life in Primary Care
Bibliographic record
Abstract
BACKGROUND: Allergic rhinitis (AR) impairs quality of life (QoL), sleep and work. The Allergic Rhinitis and its Impact on Asthma (ARIA) classification is widely used, but the impact of the different symptoms on QoL is not clear. OBJECTIVE: To describe characteristics of patients consulting in primary care for AR and to study the impact of AR symptoms and the ARIA classes on QoL. METHODS: A multicenter prospective observational cross-sectional study assessed the visual analogue scale (VAS) in the management of AR in 990 patients consulting general practitioners for AR. Patients were classified according to the four classes of ARIA. VAS, Rhinoconjunctivitis Quality of Life Questionnaire (RQLQ) and total symptom score (TSS) for nasal and non-nasal symptoms were evaluated. VAS and TSS measures were compared with RQLQ. RESULTS: Mild intermittent rhinitis was diagnosed in 20% of patients, mild persistent rhinitis in 17%, moderate/severe intermittent rhinitis in 15% and moderate/severe persistent rhinitis in 48%. The presence of treatments did not affect VAS levels. Both severity and duration of rhinitis had an impact on QoL and VAS levels. Ocular symptoms (OR: 2.78, 95% CI: 1.965-3.939) including eyelid edema (OR: 2.07, 95% CI: 1.274-3.360) and asthenia (OR: 2.73, 95% CI: 1.922-3.877) had more impact on RQLQ than nasal obstruction (OR: 1.61, 95% CI: 1.078-2.405) and nasal pruritus (OR 1.45, 95% CI: 1.028-2.042). Sneezing and rhinorrhea did not impact RQLQ. CONCLUSIONS: This study confirmed that ocular symptoms and, to a lesser degree, nasal obstruction and pruritus have a significant impact on QoL.
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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 | 0.001 | 0.006 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.001 |
| Bibliometrics | 0.001 | 0.001 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.001 | 0.000 |
| Open science | 0.000 | 0.001 |
| Research integrity | 0.001 | 0.001 |
| Insufficient payload (model declined to judge) | 0.003 | 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".