Genetic syndromes in paediatric alopecia areata: a systematic review
Bibliographic record
Abstract
Abstract Background A wide variation of phenotypes is displayed by individuals with alopecia areata (AA), especially in the paediatric population. Objectives To systematically search published studies to identify paediatric syndromes with AA and their clinical features, and to summarize the current state of their genetic elucidation. Methods In accordance with the PRISMA guidelines, a systematic search of MEDLINE, Embase, CENTRAL and PubMed databases was performed. All original case reports, case series and observational studies describing AA in children (aged <18 years) with monogenic or chromosomal syndromes were included. Further searches in OMIM and Orphanet, and reviews, clinical guidelines and basic science studies were used to retrieve additional comprehensive information on each syndrome. Results After title and abstract screening of 1426 studies, and full-text review of 224 studies, 64 met the inclusion criteria and are summarized in this review. Overall, the search identified 33 genetic syndromes with paediatric AA. Prevalence estimates were available for 79% (n = 26/33) of syndromes, with 45% (n = 15/33) of syndromes presenting in fewer than 1/1 000 000 individuals. Sixty-seven per cent (n = 22/33) of syndromes were fully genetically elucidated; 12% (n = 4/33) were partially elucidated; 9% (n = 3/33) were not genetically elucidated; and 12% (n = 4/33) were syndromes with chromosomal abnormalities. Seventy-nine per cent (n = 26/33) of syndromes were described by only one report, while 21% (n = 7/33) were described in multiple independent reports. Conclusions Despite the limited knowledge of these syndromes, this review provides insights into the range of genetic syndromes with paediatric AA and their clinical features, facilitating early prediction, diagnosis and personalized treatments.
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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 | 0.002 | 0.009 |
| Meta-epidemiology (narrow) | 0.001 | 0.000 |
| Meta-epidemiology (broad) | 0.004 | 0.003 |
| Bibliometrics | 0.008 | 0.010 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.001 | 0.001 |
| Open science | 0.001 | 0.001 |
| Research integrity | 0.001 | 0.000 |
| Insufficient payload (model declined to judge) | 0.003 | 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".