Atopic Dermatitis and Self-Image Design: A Real-Life Study in Children Using Drawings
Bibliographic record
Abstract
Abstract: Background: Atopic dermatitis (AD) induces alterations of external appearance and self-esteem, with impact on the personal development of the children. However, tools for estimating such suffering are lacking. We aimed to assess how children with AD represent themselves through their drawings. Methods: In this retrospective study, we included children (<18 years) suffering from AD who followed the instruction “draw yourself with and without eczema” at the end of a routine follow-up consultation. Drawings were interpreted with the child and then classified in different analysis groups by 5 independent evaluators. Results: A total of 64 children (41 [64.1%] girls and 23 [35.9%] boys, median [range] age 8 [3–7] years) made 64 drawings. Five groups of drawing were identified: “amputee” ( n = 8, 12.5%), “identical” ( n = 18, 28.1%), “sad” ( n = 19, 29.7%), “complex” ( n = 11, 17.2%), and “other” ( n = 8, 12.5%). Univariate analysis found that age was differently distributed among the different drawing groups ( P = 0.0047), as was the predominance of light colors ( P = 0.038). The distribution of the other variables (gender, investigator global assessment score, active AD, and duration of activity) was not different among drawing groups. Conclusions: The drawing allows a majority of the AD children to express their self-image with and without eczema, as well as their feelings and their interactions with the environment and with their entourage. The visual tool proposed herein could be used during consultations, to ( a ) become aware of the need to treat AD, ( b ) better evaluate the impact of AD burden in childhood, and ( c ) adjust appropriately AD treatment.
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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 | 0.004 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.001 |
| Bibliometrics | 0.001 | 0.001 |
| Science and technology studies | 0.001 | 0.001 |
| Scholarly communication | 0.001 | 0.001 |
| Open science | 0.000 | 0.001 |
| Research integrity | 0.001 | 0.001 |
| 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".