Management of chronic spontaneous urticaria (CSU): a treat to target approach using a patient reported outcome
Bibliographic record
Abstract
BACKGROUND: Treat-to-target therapy approaches are established for chronic diseases such as diabetes, hypertension, and more recently rheumatoid arthritis, resulting in improved patient outcomes. These approaches do not use patient reported outcomes (PRO) as targets of therapy. Chronic spontaneous urticaria (CSU), also called chronic idiopathic urticaria (CIU), is defined as recurrent urticaria of known and unknown cause, lasting more than 6 weeks. Treatment of CSU can be challenging. However, with the advent of proven therapies and validated instruments for measuring disease activity, the concept of treat-to-target (T2T) can be successfully applied to CSU. Herein, we propose a potential PRO therapeutic target and suggest a T2T approach for the management of patients with CSU. METHODS: Principles and recommendations for a treat-to-target approach in CSU (T2T/CSU) were developed by a Canadian task force, consisting of dermatologists, immunologists, and allergists. The task force formulated recommendations for therapeutic targets in CSU on the basis of a systematic literature review and expert opinion. RESULTS: The key features of these T2T/CSU recommendations are the use of a PRO as the principal target, with symptom control as measured by Urticaria Activity Score 7 (UAS7 ≤ 6), targeting symptom remission (UAS7 = 0). CONCLUSION: Treatment targets such as UAS7 ≤ 6 and UAS7 = 0 provide a benchmark for success in the care of patients with CSU, and will permit the evaluation of a PRO-based T2T approach in the care of these patients and the effect of this approach on improved patient care as seen in other chronic diseases.
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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 | 0.059 | 0.068 |
| Meta-epidemiology (narrow) | 0.001 | 0.000 |
| Meta-epidemiology (broad) | 0.004 | 0.005 |
| Bibliometrics | 0.006 | 0.004 |
| Science and technology studies | 0.001 | 0.001 |
| Scholarly communication | 0.004 | 0.004 |
| Open science | 0.002 | 0.003 |
| Research integrity | 0.002 | 0.004 |
| Insufficient payload (model declined to judge) | 0.002 | 0.000 |
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".