Definition, aims, and implementation of <scp>GA</scp><sup>2</sup><scp>LEN</scp> Urticaria Centers of Reference and Excellence
Bibliographic record
Abstract
BACKGROUND: GA²LEN, the Global Allergy and Asthma European Network, has recently launched a program for the development, interaction, and accreditation of centers of reference and excellence in special areas of allergy embedded in its overall quality management of allergy centers of excellence. The first area chosen is urticaria. Urticaria is a common and debilitating condition and can be a challenge for both patients and treating physicians, especially when chronic. Centers of reference and excellence in urticaria (UCAREs) can help to improve the management of hard-to-treat conditions such as urticaria. AIMS: Here, we describe the aims, the requirements and deliverables, the application process, and the audit and accreditation protocol for GA²LEN UCAREs. RESULTS: The main aims of GA²LEN UCAREs are to provide excellence in urticaria management, to increase the knowledge of urticaria by research and education, and to promote the awareness of urticaria by advocacy activities. To become a certified GA²LEN UCARE, urticaria centers have to apply and fulfill 32 requirements, defined by specific deliverables that are assessed during an audit visit. DISCUSSION AND CONCLUSION: The GA²LEN UCARE program will result in a strong network of urticaria specialists, promote urticaria research, and harmonize and improve urticaria management globally.
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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 | 0.112 | 0.104 |
| Meta-epidemiology (narrow) | 0.001 | 0.001 |
| Meta-epidemiology (broad) | 0.001 | 0.001 |
| Bibliometrics | 0.004 | 0.003 |
| Science and technology studies | 0.004 | 0.004 |
| Scholarly communication | 0.010 | 0.004 |
| Open science | 0.004 | 0.010 |
| Research integrity | 0.003 | 0.005 |
| Insufficient payload (model declined to judge) | 0.007 | 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".