Validation of IGA*BSA in Assessing Disease Severity and Response in Patients with Atopic Dermatitis: A Retrospective Cohort Study in China
Bibliographic record
Abstract
Abstract: Background: Accurate evaluation of atopic dermatitis (AD) severity is crucial to determine and adjust treatment options. Previous studies have found the product of Investigator’s Global Assessment (IGA) and affected body surface area (BSA) to be a simple tool, which requires further verification. Objective: To determine the validity of IGA*BSA in assessing the severity of AD across all age, sex, BMI and disease severity groups. Method: We performed a retrospective study of AD using data from a national cohort (China Type II Inflammatory Skin Disease Clinical Research and Standardized Diagnosis and Treatment Project). Results: Overall, 3051 participants were included in the final analysis. IGA*BSA correlated better with objective measures than with subjective measures. IGA*BSA significantly correlated with Eczema Area and Severity Index (EASI) (r = 0.81), which was stronger than either IGA or BSA alone with EASI, regardless of age, sex, Body Mass Index (BMI), and disease severity groups. Besides, IGA*BSA mild, moderate, and severe groups were associated with significantly higher scores of other assessments and had moderate to fair concordance with other assessments severity strata. At follow-up, the concordance of improvement between IGA*BSA 50/75/90 and EASI 50/75/90 was observed (ĸ = 0.65, 0.62, 0.58, respectively). Conclusion: IGA*BSA appears to be a valid objective assessment of AD severity and improvement over time across all age, sex, BMI, and disease severity subgroups in the clinical practice.
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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 | 0.003 | 0.002 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.001 | 0.001 |
| Bibliometrics | 0.001 | 0.001 |
| Science and technology studies | 0.001 | 0.001 |
| Scholarly communication | 0.001 | 0.000 |
| Open science | 0.001 | 0.001 |
| Research integrity | 0.001 | 0.000 |
| 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".