<scp>BSACI</scp> guideline for the diagnosis and management of allergic and non‐allergic rhinitis (Revised Edition 2017; First edition 2007)
Bibliographic record
Abstract
This is an updated guideline for the diagnosis and management of allergic and non-allergic rhinitis, first published in 2007. It was produced by the Standards of Care Committee of the British Society of Allergy and Clinical Immunology, using accredited methods. Allergic rhinitis is common and affects 10-15% of children and 26% of adults in the UK, it affects quality of life, school and work attendance, and is a risk factor for development of asthma. Allergic rhinitis is diagnosed by history and examination, supported by specific allergy tests. Topical nasal corticosteroids are the treatment of choice for moderate to severe disease. Combination therapy with intranasal corticosteroid plus intranasal antihistamine is more effective than either alone and provides second line treatment for those with rhinitis poorly controlled on monotherapy. Immunotherapy is highly effective when the specific allergen is the responsible driver for the symptoms. Treatment of rhinitis is associated with benefits for asthma. Non-allergic rhinitis also is a risk factor for the development of asthma and may be eosinophilic and steroid-responsive or neurogenic and non- inflammatory. Non-allergic rhinitis may be a presenting complaint for systemic disorders such as granulomatous or eosinophilic polyangiitis, and sarcoidoisis. Infective rhinitis can be caused by viruses, and less commonly by bacteria, fungi and protozoa.
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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) | 0.001 | 0.000 |
| Meta-epidemiology (broad) | 0.001 | 0.002 |
| Bibliometrics | 0.002 | 0.002 |
| Science and technology studies | 0.001 | 0.001 |
| Scholarly communication | 0.002 | 0.002 |
| Open science | 0.002 | 0.001 |
| Research integrity | 0.003 | 0.004 |
| Insufficient payload (model declined to judge) | 0.050 | 0.051 |
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".