EUFOREA meeting on defining disease states in allergic rhinitis: towards a unified language in AR
Bibliographic record
Abstract
Allergic rhinitis (AR), the most prevalent immunological disease, affects approximately 400 million individuals globally and can significantly impact quality of life (QoL). Despite nearly 25 years of guidelines, AR remains largely under- diagnosed, suboptimally treated and poorly controlled. In the light of new knowledge and treatment options, there is a necessity to update or revise fundamental AR definitions to facilitate communication across diverse specialties engaged in its treatment and to improve patient care. The European Forum for Research and Education in Allergy and Airway Diseases (EUFOREA) convened a meeting of experts and patient representatives to deliberate the optimal methodology for measuring AR treatment responses and establishing novel treatment goals. This paper presents a consensus on revised AR definitions, including control, severe allergic rhinoconjunctivitis (SARC), refractory severe allergic rhinoconjunctivitis (R-SARC), remission, resolution, improvement, exacerbation, treatable traits (TTs), treat to target, relapse, progression, disease modification, and prevention.
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 | 0.077 | 0.044 |
| Meta-epidemiology (narrow) | 0.002 | 0.001 |
| Meta-epidemiology (broad) | 0.003 | 0.004 |
| Bibliometrics | 0.003 | 0.002 |
| Science and technology studies | 0.003 | 0.005 |
| Scholarly communication | 0.008 | 0.006 |
| Open science | 0.007 | 0.010 |
| Research integrity | 0.020 | 0.025 |
| Insufficient payload (model declined to judge) | 0.005 | 0.003 |
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".