Controversies and challenges in the management of chronic urticaria
Bibliographic record
Abstract
This supplement reports proceedings of the second international Global Urticaria Forum, which was held in Berlin, Germany in November 2015. Despite the clear international guideline, there remain a number of controversies and challenges in the management of patients with chronic urticaria (CU). As a result of major advancements in urticaria over the past 4 years, the current EAACI/GA(2) LEN/EDF/WAO urticaria guideline treatment algorithm requires updating. Case studies from patients with chronic spontaneous urticaria (CSU) [also called chronic idiopathic urticaria (CIU)], chronic inducible urticaria (CIndU) or diseases and syndromes related to CU are useful in describing and exploring challenges in disease management. Case studies of specific CSU patient populations such as children with CU or patients with angio-edema but no hives also require consideration as potentially challenging groups with unmet needs. The current EAACI/GA(2) LEN/EDF/WAO urticaria guideline provides a general framework for the management of patients with CU but, as these cases highlight, a personalized approach based on the expert knowledge of the physician may be required.
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 | 0.002 | 0.005 |
| Meta-epidemiology (narrow) | 0.001 | 0.000 |
| Meta-epidemiology (broad) | 0.002 | 0.001 |
| Bibliometrics | 0.003 | 0.003 |
| Science and technology studies | 0.001 | 0.001 |
| Scholarly communication | 0.002 | 0.003 |
| Open science | 0.001 | 0.001 |
| Research integrity | 0.002 | 0.003 |
| Insufficient payload (model declined to judge) | 0.003 | 0.001 |
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".