Discordance Between Diagnosis Tools for Assessing Eczema in Infants: A Challenge for Intervention Trials
Bibliographic record
Abstract
BACKGROUND: There is no standardized definition for infant eczema, and various tools have been used across studies, precluding direct comparison. OBJECTIVE: The aim of the study was to assess and to compare the accuracy of diagnostic tools for infant eczema using the extensive data collected in Melbourne Infant Study: BCG for Allergy and Infection Reduction (MIS BAIR), an eczema prevention trial. METHODS: Eczema incidence was assessed by 3 questionnaire-based measures: modified UK diagnostic tool, parent-reported medically diagnosed eczema, and parent-reported use of topical corticosteroids. Agreement between the definitions was quantified using κ coefficient. Eczema severity was assessed by 3-monthly Patient-Oriented Eczema Measure (POEM) scores and a SCORing Atopic Dermatitis (SCORAD) clinical assessment at a 12-month visit (ClinicalTrial.gov: NCT01906853). RESULTS: Among the 538 participants fulfilling at least 1 of the 3 questionnaire-based eczema definitions, only 197 participants (37%) met all 3 definitions. Agreement between the definitions was poor with κ coefficients ranging from -0.11 to 0.62. The most frequently reported symptoms were generally dry skin (483/538, 90%) and pruritus (400/538, 74%). The face (352/538, 65%) and the trunk (306/538, 57%) were more frequently affected than the creases (257/538, 48%). Participants fulfilling all 3 questionnaire-based definitions of eczema were more likely to have higher severity scores and earlier onset of symptoms. CONCLUSIONS: There is poor agreement between currently available tools for assessing infant eczema.
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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 | 0.669 | 0.638 |
| Meta-epidemiology (narrow) | 0.003 | 0.002 |
| Meta-epidemiology (broad) | 0.012 | 0.011 |
| Bibliometrics | 0.003 | 0.005 |
| Science and technology studies | 0.003 | 0.008 |
| Scholarly communication | 0.011 | 0.012 |
| Open science | 0.010 | 0.007 |
| Research integrity | 0.009 | 0.009 |
| 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".