Interobserver Agreement of the Eczema Area and Severity Index for Atopic Dermatitis Severity Assessment: A Real-World Evidence Study
Bibliographic record
Abstract
Abstract: Background: The Eczema Area and Severity Index (EASI) has been suggested to show a good to excellent level of concordance. However, in everyday clinical practice, there is a perceived low level of concordance. Objective: To assess the degree of concordance in the EASI scores in a real-world clinical setting. Methods: This transversal study assesses interobserver concordance of EASI among 5 evaluators between January and October 2024. Results: For the head and neck area, unacceptable concordance was found for all the items in EASI. In the trunk area, there was acceptable concordance for the affected surface area (0.686 [95% confidence interval {CI}: 0.539–0.799]) but unacceptable concordance for the other items. For the upper and lower extremities, unacceptable concordance was observed for all items. The total EASI interclass correlation coefficient showed moderate concordance (0.754 [95% CI: 0.659–0.838]). Despite moderate concordance for the total EASI score, the median EASI range for the same patient was 13, indicating significant heterogeneity between evaluators. Discussion and Conclusion: This study highlights the variability of the EASI in the evaluation of atopic dermatitis (AD) in a real-world clinical practice setting, suggesting that patient-reported outcome measures and experiences should be given more importance as additional AD severity measures.
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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 | 0.115 | 0.209 |
| Meta-epidemiology (narrow) | 0.001 | 0.001 |
| Meta-epidemiology (broad) | 0.002 | 0.003 |
| Bibliometrics | 0.004 | 0.003 |
| Science and technology studies | 0.001 | 0.003 |
| Scholarly communication | 0.003 | 0.002 |
| Open science | 0.002 | 0.002 |
| Research integrity | 0.002 | 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".