Vitiligo: concomitant autoimmune and allergic diseases
Bibliographic record
Abstract
Introduction:The aim of the study was to determine its epidemiology and clinical aspects.Vitiligo is a chronic skin condition caused by progressive cutaneous hypomelanosis.Aim: As considerable progress has been made in understanding of the pathogenesis of vitiligo and its classification as an autoimmune disease, the paper pays particular attention to coexisting autoimmune or atopic diseases. Material and methods:The study included 55 patients attending the Diagnostic and Treatment Center of Skin Diseases in Lodz.Data were collected during outpatient dermatological consultation.Results: The most common type of vitiligo was nonsegmental (85.5%) followed by segmental (12.7%) and unclassified (2.1%).The first skin lesions were mostly located on the hands (45.5%) and face (38.2%).Older patients with higher body mass index tended to demonstrate a higher body surface area.Of the patients, 63.6% demonstrated an autoimmune or atopic comorbidity, the most common of which were type 1 diabetes mellitus (18.2%), psoriasis (16.4%) or Hashimoto's thyroiditis (14.5%).Location on the face was associated with a significantly greater incidence of autoimmune or atopic co-morbidities.Conclusions: A facial location may serve as a predictive factor for other autoimmune or atopic diseases in vitiligo patients.Determining clinical factors in vitiligo patients which could be associated with a higher risk of autoimmune comorbidities may allow for their early diagnosis and suitable 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 | 0.002 |
| Meta-epidemiology (narrow) | 0.001 | 0.000 |
| Meta-epidemiology (broad) | 0.001 | 0.001 |
| Bibliometrics | 0.002 | 0.002 |
| Science and technology studies | 0.001 | 0.001 |
| Scholarly communication | 0.002 | 0.002 |
| Open science | 0.000 | 0.002 |
| Research integrity | 0.001 | 0.003 |
| Insufficient payload (model declined to judge) | 0.011 | 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".