Diagnosing atopic dermatitis in infancy: Questionnaire reports vs criteria‐based assessment
Bibliographic record
Abstract
BACKGROUND: Persisting atopic dermatitis (AD) is known to be associated with more serious allergic diseases at later ages; however, making an accurate diagnosis during infancy is challenging. We assessed the diagnostic performance of questionnaire-based AD measures with criteria-based in-person clinical assessments at age 1 year and evaluated the ability of these diagnostic methods to predict asthma, allergic rhinitis and food allergies at age 5 years. METHODS: Data relate to 3014 children participating in the Canadian Healthy Infant Longitudinal Development (CHILD) Study who were directly observed in a clinical assessment by an experienced healthcare professional using the UK Working Party criteria. The majority (2221; 73.7%) of these children also provided multiple other methods of AD ascertainment: a parent reporting a characteristic rash on a questionnaire, a parent reporting the diagnosis provided by an external physician and a combination of these two reports. RESULTS: Relative to the direct clinical assessment, the area under the Receiver Operating Characteristic curve for a parental report of a characteristic rash, reported physician diagnosis and a combination of both were, respectively, 0.60, 0.69 and 0.70. The strongest predictor of asthma at 5 years was AD determined by criteria-based in-person clinical assessment followed by the combination of parental and physician report. CONCLUSIONS: These findings suggest that questionnaire data cannot accurately substitute for assessment by experienced healthcare professionals using validated criteria for diagnosis of atopic dermatitis. Combining the parental report with diagnosis by a family physician might sometimes be appropriate (eg to avoid costs of a clinical assessment).
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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 | 0.024 | 0.052 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.002 | 0.001 |
| 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) | 0.001 | 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".