The burden of allergic rhinitis (AR) in Canada: perspectives of physicians and patients
Bibliographic record
Abstract
BACKGROUND: Allergic rhinitis (AR) is a common problem and we sought to examine the burden of disease and its management in Canada from the perspectives of patients and physicians. METHODS: Two parallel, Canadawide structured telephone interviews surveyed 1,001 AR patients and 160 physicians in July 2006. RESULTS: 44% of patients had experienced nasal symptoms unrelated to a cold and 20% had a physician diagnosis of AR. At screening 27% reported asthma, 15% chronic or recurrent sinusitis and 5% nasal polyps. With attacks nasal congestion and runny nose were the most bothersome symptoms. Other problems experienced were fatigue (46%), poor concentration (32%), and reduced productivity (23%). Most (77%) had not seen a physician in the past year. Physicians estimated they prescribed intranasal cortico steroids (INCS) to most AR patients (77%) consistent with guidelines but only 19% of patients had used one in the last month. Only 48% of patients were very satisfied with their current INCS. 41% of AR patients reported discontinuing their INCS with the most common reason being a perceived lack of long-lasting symptom relief (44%). 52% of patients felt that their current INCS lost effectiveness over 24 h. The most common INCS side effects included dripping down the throat, bad taste, and dryness. Most AR patients reported lifestyle limitations despite treatment (66%). 61% of patients felt that their symptoms were only somewhat controlled or poorly/not controlled during their worst month in the past year. CONCLUSIONS: AR symptoms are common and many patients experience inadequate control. Physicians report they commonly prescribe intranasal corticosteroids, but patient's perceived loss of efficacy and side effects lead to their discontinuation. Persistent relief of allergic rhinitis symptoms remains a major unmet need. Better treatments and education are required.
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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 | 0.003 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.001 | 0.002 |
| Science and technology studies | 0.005 | 0.001 |
| Scholarly communication | 0.002 | 0.001 |
| 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".