2019 ARIA: care pathways for allergic rhinitis in Russia
Bibliographic record
Abstract
One of the most common chronic upper respiratory diseases worldwide is allergic rhinitis (AR). Despite advances in understanding the mechanisms of allergic inflammation, the symptoms of AR in most cases are not completely controlled by modern treatment methods. Allergic rhinitis is a precursor and predisposing factor for the development of other respiratory diseases, one of which is asthma. Diagnosis of AR is being actively conducted, but there is still a serious problem of uncontrolled and chaotic treatment of patients, so it is necessary to provide comprehensive medical care within the national health system. ARIA aims to develop and apply internationally recommendations for the management of patients with allergic respiratory diseases. In collaboration with other international associations that deal with the treatment and diagnosis of allergies and respiratory diseases, regulations and programs have been developed for the treatment of patients with AR, as well as when it is combined with asthma, which form the basis of ARIA. This document has been adapted for use in the field of healthcare in the Russian Federation and covers key issues related to the management of patients with AR and in combination with AR and asthma.
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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 | 0.002 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.001 |
| Bibliometrics | 0.001 | 0.000 |
| Science and technology studies | 0.001 | 0.000 |
| Scholarly communication | 0.001 | 0.001 |
| Open science | 0.001 | 0.002 |
| Research integrity | 0.001 | 0.001 |
| Insufficient payload (model declined to judge) | 0.027 | 0.005 |
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".