Cold urticaria in a pediatric cohort: Clinical characteristics, management, and natural history
Bibliographic record
Abstract
Abstract Background Cold urticaria (coldU) is associated with substantial morbidity and risk of fatality. Data on coldU in children are sparse. We aimed to evaluate the clinical characteristics, management, risk of associated anaphylaxis, and resolution rate of coldU in a pediatric cohort. Additionally, we sought to compare these metrics to children with chronic spontaneous urticaria (CSU). Methods We prospectively enrolled children with coldU from 2013–2021 in a cohort study at the Montreal Children's Hospital and an affiliated allergy clinic. Data for comparison with participants with solely CSU were extracted from a previous study. Data on demographics, comorbidities, severity of presentation, management, and laboratory values were collected at study entry. Patients were contacted yearly to assess for resolution. Results Fifty‐two children with cold urticaria were recruited, 51.9% were female and the median age of symptom onset was 9.5 years. Most patients were managed with second‐generation H1‐antihistamines (sgAHs). Well‐controlled disease on sgAHs was negatively associated with concomitant CSU (adjusted odds ratio (aOR) = 0.69 [95%CI: 0.53, 0.92]). Elevated eosinophils were associated with cold‐induced anaphylaxis (coldA; aOR = 1.38 [95%CI: 1.04, 1.83]), which occurred in 17.3% of patients. The resolution rate of coldU was 4.8 per 100 patient‐years, which was lower than that of CSU (adjusted hazard ratio = 0.43 [95%CI: 0.21, 0.89], p < 10−2). Conclusion Pediatric coldU bears a substantial risk of anaphylaxis and a low‐resolution rate. Absolute eosinophil count and co‐existing CSU may be useful predictive factors.
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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 | 0.001 | 0.002 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.001 | 0.001 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.001 | 0.001 |
| Open science | 0.000 | 0.000 |
| Research integrity | 0.000 | 0.001 |
| Insufficient payload (model declined to judge) | 0.001 | 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".