An Update on the Cutaneous Manifestations of Darier Disease
Bibliographic record
Abstract
Background Knowledge about the clinical features of Darier disease, an orphan autosomal-dominant genetic disorder, is sparse and has been evaluated only in few studies. Objectives To investigate the clinical features of a large group of patients with Darier disease, and to explore for associations between disease characteristics and severity of the disease. Methods Seventy-six individuals with Darier disease were evaluated utilizing a structured questionnaire-based interview, a physical examination, and a retrospective assessment of their medical records. Results The most frequent locations of lesions were hands (99%) and fingernails (93%). Wart-like lesions on the hands were more visible after soaking them in water for 5 minutes, we therefore named this phenomenon the “wet hand sign”. Oral involvement was found in 43% of patients, while 48% of women and 16% of men showed genital lesions. Patients with severe Darier disease had a tenfold greater risk of developing genital lesions than those with mild disease ( P = .01). Most patients (88%) in our study exhibited a combination of the four types of the disease patterns of distribution (flexural, seborrheic, nevoid, and acral). Conclusions Documentation of disease on the hands and fingernails provides a highly sensitive means to aid in the diagnosis of Darier disease. It is important to evaluate mucosal lesions including genital and oral mucosa.
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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) | 0.001 | 0.000 |
| Meta-epidemiology (broad) | 0.002 | 0.001 |
| Bibliometrics | 0.004 | 0.002 |
| Science and technology studies | 0.000 | 0.001 |
| Scholarly communication | 0.002 | 0.002 |
| Open science | 0.001 | 0.001 |
| Research integrity | 0.002 | 0.003 |
| Insufficient payload (model declined to judge) | 0.005 | 0.002 |
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".