Diagnostic testing for chronic spontaneous urticaria with or without angioedema: The do's, don't and maybe's
Bibliographic record
Abstract
Chronic spontaneous urticaria (CSU), with or without angioedema, is heterogeneous and comprised of different endotypes and phenotypes. Because acute urticaria will mostly resolve spontaneously, routine testing and laboratory evaluation is not required unless supported by the clinical history or physical examination. With the advent of omalizumab, there has been a surge of interest in identifying biomarkers that could predict response to this treatment. In the process of investigating biomarkers as prognosticators, several CSU phenotypes and endotypes have emerged, which have made it evident that novel therapies targeting non-IgE mechanistic pathways are needed to control symptoms in patients unresponsive to the currently recommended therapies by the most recent international guidelines. The current data support peripheral eosinophils, autoantibodies against IgE or FcεRI α subunit measured by basophil histamine release assays, total IgE levels and IgG autoantibodies against thyroid peroxidase (TPO) as specific markers to differentiate type 1 autoimmune (autoallergic) CSU from type 2b autoimmune CSU before starting treatment especially with omalizumab. These markers have been included as exploratory endpoints in many clinical trials investigating novel therapies or for repurposing existing biologics to determine responders and non-responders, but these data are not completely clear at this time. Therefore, further randomized controlled studies and real-world studies are needed to demonstrate more conclusively the utility of ordering these tests in CSU patients when they initially present or when it is determined they are not responsive to high dose second generation H1-antihistamines (SGAH) before they can be included in evidence-based CSU guidelines. This review examines the value of obtaining diagnostic tests in the initial evaluation of CSU patients to predict treatment response.
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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 | 0.006 |
| Meta-epidemiology (narrow) | 0.001 | 0.000 |
| Meta-epidemiology (broad) | 0.001 | 0.001 |
| Bibliometrics | 0.002 | 0.001 |
| Science and technology studies | 0.000 | 0.001 |
| Scholarly communication | 0.001 | 0.001 |
| Open science | 0.001 | 0.000 |
| Research integrity | 0.002 | 0.001 |
| 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".