Development of an Eczema Area and Severity Index Atlas for Diverse Skin Types
Bibliographic record
Abstract
Abstract: Background: Current guidance for using Eczema Area and Severity Index (EASI) implementation is limited to lighter skin phototypes. We developed an EASI lesion severity atlas and refined guidance for investigators and clinicians to use across diverse patient populations. Methods: A review was performed of clinical images from internal atopic dermatitis (AD) photorepositories. Representative images of the 4 AD signs included in EASI were selected for different physician-assessed skin phototypes. Images were excluded if they had low resolution, poor focus, or lighting. Discrepancies regarding skin pigmentation and AD severity were resolved by consensus between authors. Results: Over 3000 clinical photographs were reviewed. Final images were selected using an iterative review process and consensus. Two different versions of the atlas were created across 6 physician-assessed phototypes (I–VI) and 3 skin complexions (light, medium, and dark). We propose guidance language for erythema to reflect the range of colors encountered across different skin complexions (shades of red, purple, and brown). Conclusion: We created a photographic atlas and updated guidance language for implementing EASI in diverse populations, including those with higher skin phototypes.
Fetched live from OpenAlex and de-inverted. Abstracts are not stored in this database: the inverted indexes are 8.6 GB of the frame’s 9.3 GB of text, and the host has 13 GB free.
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 | 0.017 | 0.043 |
| Meta-epidemiology (narrow) | 0.001 | 0.001 |
| Meta-epidemiology (broad) | 0.001 | 0.002 |
| Bibliometrics | 0.016 | 0.006 |
| Science and technology studies | 0.001 | 0.001 |
| Scholarly communication | 0.003 | 0.004 |
| Open science | 0.002 | 0.002 |
| Research integrity | 0.001 | 0.002 |
| Insufficient payload (model declined to judge) | 0.007 | 0.004 |
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".