Successful Dupilumab Administration in Adolescent with Alopecia Areata and Atopic Dermatitis: Clinical Case
Bibliographic record
Abstract
Background. Alopecia areata is an autoimmune disease characterized by hair loss that develops with the involvement of CD8+ T-cells of the immune system and cytokines produced by T-helper 2 cells (Th2). Efficacy of alopecia areata management is limited. The one potentially effective variant for treatment of severe forms of alopecia areata complicated with atopic dermatitis (AD) is genetically engineered biologic drug dupilumab (interleukin 4 receptor antagonist that suppresses Th2 response). Clinical case description. 11 years old boy was admitted to the dermatology department with complaints on aggravation of AD and numerous hair loss foci. Dupilumab, 300 mg, was prescribed once every 4 weeks. Severity of erythematous papular rashes significantly decreased within 9 months, as well as irritation intensity. Complete restoration of hair growth was noted in areas of former alopecia areata foci. Conclusion. Dupilumab can be effective in the management of severe forms of alopecia areata in children with comorbid AD. Clinical studies on the efficacy and safety of such therapy are needed to confirm this hypothesis.
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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 | 0.000 | 0.001 |
| Meta-epidemiology (narrow) | 0.001 | 0.000 |
| Meta-epidemiology (broad) | 0.001 | 0.001 |
| Bibliometrics | 0.001 | 0.000 |
| Science and technology studies | 0.001 | 0.001 |
| Scholarly communication | 0.001 | 0.001 |
| Open science | 0.001 | 0.001 |
| Research integrity | 0.003 | 0.002 |
| 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".