Quality of life, emotion dysregulation, attention deficit and psychiatric comorbidity in children and adolescents with vitiligo
Bibliographic record
Abstract
BACKGROUND: Vitiligo is an acquired pigmentation disorder, which can have a negative effect on patient quality of life (QoL). AIM: To evaluate QoL and psychiatric comorbidity in paediatric patients with vitiligo. METHODS: In total, 30 patients aged 8-18 years who were diagnosed with vitiligo and 30 age- and sex-matched healthy controls (HCs) were included in the study. The Children's Depression Inventory, Screen for Child Anxiety Related Disorders, State-Trait Anxiety Inventory for Children and Child Somatization Inventory were completed for both patients and controls. The Schedule for Affective Disorders and Schizophrenia for School Age Children-Present and Lifetime Version (K-SADS-PL) was administered to all patients by a child psychiatrist. Families were also asked to complete the Pediatric Quality of Life Inventory and Emotion Regulation Checklist for children. RESULTS: The K-SADS-PL evaluation showed that 90% of the patients in the vitiligo group had at least one psychiatric diagnosis, whereas this rate was 20% in the HCs (P < 0.001). There were statistically significant differences between vitiligo and HCs in terms of anxiety, state and trait anxiety scores (P < 0.05). Attention deficit and hyperactivity disorder (ADHD) was detected in 36.6% of the patients. CONCLUSION: The most important finding of this study is that anxiety disorders are more prominent than depression in childhood vitiligo. Another important finding of this study is that the prevalence of ADHD is significantly higher than the normal population.
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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) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.001 | 0.001 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.000 | 0.000 |
| Open science | 0.000 | 0.000 |
| Research integrity | 0.000 | 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".