Evaluation of clinical scales among populations diagnosed with atopic dermatitis: A scoping review
Bibliographic record
Abstract
Abstract Background Common diagnostic tools for atopic dermatitis (AD) often perform worse in skin-of-colour (SOC) populations. The objective of this review is to map the prevalence, validation, and effectiveness of clinician-based and patient-reported tools for diagnosing AD in SOC groups across all ages. Methods This review followed PRISMA-ScR guidelines and searched Embase, Scopus, PubMed, MEDLINE, Web of Science, and MedRxiv for articles published January 2015 through December 2023. Eligible studies were observational, randomized, or review articles evaluating clinician-rated scales or patient-reported measures with self-identified race or ethnicity. We excluded non-English publications, case reports/series, guidelines, editorials, and studies lacking stratification. After de-duplication, two reviewers screened titles, abstracts, and full texts with conflicts resolved by a third reviewer. Data extraction captured study design, population demographics, tools evaluated, and key findings on accuracy and reliability in SOC cohorts. Results 28 articles (total n = 20 332) met inclusion criteria. 24 assessed clinician-rated scales, most often EASI (n = 16), SCORAD (n = 10), and o-SCORAD (n = 8). These tools frequently underestimate AD severity in Fitzpatrick IV-VI skin types. Five studies examined alternative clinician tools (vIGA-AD, IGAxBSA). Rajka-Langeland and ADSI scores were each assessed once. Patient-reported outcomes (PROs) were dominated by POEM (n = 17), which had only 14% SOC participants during initial validation. PO-SCORAD (a PRO based on SCORAD) was also assessed (n = 10). Nine newer PRO tools (RECAP, ADCT, PSAAD, ADSEQ, CEQ, DFI, CADIS, QoLIAD, PIQoL-AD) appeared in single studies. Adjunctive measures and technological approaches (body-surface area alone, photo guides, AI-assisted analysis, remote assessment) featured in five studies but lack multi-center validation. Conclusions Most diagnostic tools remain validated in lighter-skinned cohorts and underrepresent SOC populations. Patient-reported measures show promise but require wider validation. Adjunctive and technology-driven methods may improve equity but need rigorous testing. Future research should prioritize multiethnic cohorts, age-specific validation, and consensus-driven adaptation of both clinician and patient-reported tools to ensure reliable assessment for all skin types.
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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 | 0.035 | 0.177 |
| Meta-epidemiology (narrow) | 0.002 | 0.001 |
| Meta-epidemiology (broad) | 0.008 | 0.007 |
| Bibliometrics | 0.027 | 0.023 |
| Science and technology studies | 0.001 | 0.002 |
| Scholarly communication | 0.005 | 0.004 |
| Open science | 0.003 | 0.003 |
| Research integrity | 0.002 | 0.002 |
| 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".