Identification of categories at risk for high quality of life impairment in patients with vitiligo
Bibliographic record
Abstract
BACKGROUND: Quality of life (QoL) in patients with vitiligo is strongly impaired. Therefore, it seems inadequate to describe the severity of the disease using only physical indicators. OBJECTIVES: To investigate the QoL of patients with vitiligo, identifying categories at risk for high impairment, also analysing single questions from a QoL instrument. METHODS: The Skindex-29 questionnaire, a QoL dermatology-specific instrument, was completed by 181 consecutive patients with vitiligo. Answers to the Skindex-29 items were given on a five-point scale, from 'never' to 'all the time'. Results The QoL problems more frequently experienced 'often' or 'all the time' were: worry of the disease getting worse (60%), anger (37%), embarrassment (34%), depression (31%), having social life affected (28%), and shame (28%). The prevalence of patients with probable depression or anxiety, evaluated using the 12-item General Health Questionnaire, was 39%, and the prevalence of patients with alexithymia, evaluated using the 20-item Toronto Alexithymia Scale, was 24%. The association of QoL impairment with psychological problems was very strong for all the items, and remained significant also when taking into account simultaneously gender, age, clinical severity, family history, and localization of vitiligo. CONCLUSIONS: Detailed information on QoL in patients with vitiligo may lead dermatologists to pay particular attention to patient categories at risk for a high QoL impairment.
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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 | 0.003 |
| 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 | 0.001 |
| Research integrity | 0.000 | 0.000 |
| Insufficient payload (model declined to judge) | 0.002 | 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".