Atopic Dermatitis in Children in the General Population: Baseline Characteristics, Medication Use, and Severity Measures in the Rotterdam Eczema Study
Bibliographic record
Abstract
Abstract: Background: Real-life data on severity and treatments in children with atopic dermatitis (AD) are needed to evaluate self-management. Objectives: To determine severity and use of topical treatments in children with AD in the general population. Furthermore, we aim to determine agreement and correlation between objective and subjective AD severity measures. Methods: Data were used from the Rotterdam Eczema Study, an observational prospective cohort study with an embedded pragmatic open-label randomized controlled trial. Descriptive statistics were used for baseline characteristics, medication use, and severity. Strength of agreement and correlation were determined using kappa analysis and Pearson correlation. Results: In total, 367 children (mean age 5.7 years) were recruited. The mean eczema area and severity index (EASI) score was 2.1 (±3.2) and mean patient-oriented eczema measure (POEM) score was 10.3 (±6.1). The majority applied emollients on a daily basis (54.9%) and had not used topical corticosteroids (TCSs) over the past week (51%). Based on severity banding of POEM and EASI, 49.9% and 24.9% of the children were undertreated, respectively. No evidence was found for an agreement between EASI and POEM (kappa 0.028, n = 178, P = 0.451). A moderate correlation between POEM, EASI, infants' dermatitis quality of life index, and children's dermatology life quality index was found. POEM showed higher correlation with quality of life (QoL) than EASI. Conclusion: Emollients were used sufficiently in the study population. Based on signs or symptoms, 24.9% and 49.9% of children are undertreated, respectively. POEM scores correlated better with QoL than with EASI scores. We argue that EASI underestimates severity of AD, and treatment based on EASI scores may lead to undertreatment of AD. Treating physicians should be aware of suboptimal use of TCSs.
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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 | 0.005 |
| Meta-epidemiology (narrow) | 0.000 | 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 | 0.000 | 0.001 |
| Research integrity | 0.001 | 0.001 |
| 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".