Towards definitive management of allergic rhinitis: best use of new and established therapies
Bibliographic record
Abstract
BACKGROUND: Allergic rhinitis (AR) is an inflammatory disease of the nasal mucosa impacting up to 25% of Canadians. The standard of care for AR includes a treatment plan that takes into account patient preferences, the severity of the disease, and most essentially involves a shared decision-making process between patient and provider. BODY: Since their introduction in the 1940s, antihistamines (AHs) have been the most utilized class of medications for the treatment of AR. First-generation AHs are associated with adverse central nervous system (CNS) and anticholinergic side effects. On the market in the 1980s, newer generation AHs have improved safety and efficacy. Compared to antihistamines, intranasal corticosteroids (INCS) have significantly greater efficacy but longer onset of action. Intranasal AH and INCS combinations offer a single medication option that offers broader disease coverage and faster symptom control. However, cost and twice-per-day dosing remain a major limitation. Allergen immunotherapy (AIT) is the only disease-modifying option and can be provided through subcutaneous (SCIT) or sublingual (SLIT) routes. While SCIT has been the definitive management option for many years, SLIT tablets (SLIT-T) have also been proven to be safe and efficacious. CONCLUSION: There is a range of available treatment options for AR that reflect the varying disease length and severity. For mild to moderate AR, newer generation AHs should be the first-line treatment, while INCS are mainstay treatments for moderate to severe AR. In patients who do not respond to INCS, a combination of intranasal AH/INCS (AZE/FP) should be considered, assuming that cost is not a limiting factor. While SCIT remains the option with the most available allergens that can be targeted, it has the potential for severe systemic adverse effects and requires weekly visits for administration during the first 4 to 6 months. SLIT-T is a newer approach that provides the ease of being self-administered and presents a reduced risk for systemic reactions. In any case, standard care for AR includes a treatment plan that takes into account disease severity and patient preferences.
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 | 0.005 | 0.008 |
| Meta-epidemiology (narrow) | 0.001 | 0.000 |
| Meta-epidemiology (broad) | 0.002 | 0.002 |
| Bibliometrics | 0.002 | 0.001 |
| Science and technology studies | 0.001 | 0.002 |
| Scholarly communication | 0.005 | 0.006 |
| Open science | 0.002 | 0.002 |
| Research integrity | 0.004 | 0.009 |
| Insufficient payload (model declined to judge) | 0.010 | 0.006 |
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".