Impact of Allergic Rhinitis Symptoms on Quality of Life in Primary Care
Why this work is in the frame
A frame that forgets how it found something cannot be audited. These are the routes that admitted this work.
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.
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.
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 | 0.000 | 0.000 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 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 it