Chronic urticaria treatment challenges in children.
Bibliographic record
Abstract
OBJECTIVE: This paper aims to review the efficacy and safety of current chronic urticaria (CU) treatment in children and the existing patient-reported outcome measures (PROMs) used in this age group. DATA SOURCE: Since there are few studies of CU in children, the authors performed a non-systematic review of published articles in English, Spanish, and Portuguese in the PubMed database in the last decade. Keywords used were (antihistamines OR omalizumab OR cyclosporine OR treatment) AND (chronic urticaria) AND (children OR adolescents). DATA SYNTHESIS: According to the current guideline's algorithm, the treatment of CU involves using high doses of antihistamines when there is no response with the licensed dosage. The effectiveness of this increase in children has been demonstrated with control rates ranging from 35% to 92%, with few studies addressing safety profiles. Omalizumab is approved for children over 12 years of age as a second step in the algorithm. Although more studies with children are needed to assess its effectiveness and safety, some data show complete control of symptoms in more than 80% of pediatric cases with no adverse effects, including in children under 12 years. When monitored closely, cyclosporine showed a good response rate in pediatric CU with few adverse events. Also, PROMs validated for this age can be helpful in clinical decisions, such as Urticaria Activity Score summed over 7 days, Urticaria Control Test, and Chronic Urticaria Quality of Life Questionnaire. CONCLUSIONS: Collaborative studies are necessary to generate stronger evidence to support the guideline recommendations for children. The existing data indicate that these drugs are safe and effective for treatment when dose adjustments are made.
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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 | 0.003 | 0.011 |
| Meta-epidemiology (narrow) | 0.001 | 0.000 |
| Meta-epidemiology (broad) | 0.001 | 0.001 |
| Bibliometrics | 0.001 | 0.002 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.001 | 0.001 |
| Open science | 0.001 | 0.001 |
| Research integrity | 0.001 | 0.001 |
| Insufficient payload (model declined to judge) | 0.004 | 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".