A Retrospective Analysis of Risk Factors for Atopic Dermatitis Severity
Bibliographic record
Abstract
Abstract: Background : Atopic dermatitis (AD) has the highest burden of any skin disease; however, the severity-associated factors remain unclear. Objective: To evaluate potential severity-associated factors of AD and to design and validate a severity prediction model to inform the management of AD patients. Methods: A cross-sectional study of 900 AD patients was conducted from December 2021 to October 2022 at our hospital. The primary outcome was disease severity, categorized as mild, moderate, or severe using the scoring atopic dermatitis index. Ordinal logistic regression and bootstrapped validation were used to derive and internally validate the model. Results: Increasing age, elevated eosinophil level, higher economic status, and urban residence were associated with severe AD. Breastfeeding, disinfectants and topical emollients use, and short duration of bathing were associated with mild AD. In the prediction model, predictors included age, eosinophil and economic status, residence, feeding, disinfectants and emollients use, and duration of bathing. Prediction models demonstrated good discrimination (bias-corrected concordance index [c-index] = 0.72) and good calibration. Conclusion: Risk factors for the severity of AD were identified that could aid the early prediction of AD progression. The predictive model included variables that are easily evaluated and could inform personalized prevention and therapy.
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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 | 0.001 | 0.002 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.001 | 0.001 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.000 | 0.000 |
| Open science | 0.000 | 0.000 |
| Research integrity | 0.000 | 0.000 |
| 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".