Stigmatisation, Avoidance Behaviour and Difficulties in Coping are Common Among Adult Patients with Vitiligo
Bibliographic record
Abstract
Vitiligo is a non-contagious skin disorder with loss of pigmentation, often impairing patients' well-being. This study used Dermatology Life Quality Index (DLQI), Adjustment to Chronic Skin Disorders Questionnaire (ACS), Beck Depression Inventory (BDI) and additional questions to explore quality of life (QoL), coping, depression and stigmatisation and included 96 patients with vitiligo and 23 controls. Stigmatisation was common: 87/96 patients (90%) reported questions/approaches, 23/96 (24%) experienced nasty comments. Sixty-four out of 96 (66.7%) had avoided situations because of vitiligo or concealed their white spots. Sixty patients (62.5%) implied psychological stress as influential on disease's course. Patients scored higher in all questionnaires than controls (DLQI = 4.9/1.6, ACS-social anxiety/avoidance = 36.9/22.1, ACS-helplessness = 27.3/16.0, ACS-anxious-depressive mood = 19.4/15.6), except BDI (6.8/7.3). QoL of 65 patients (67.7%) was hardly impaired, 70 (72.9%) were not depressed. Treatment with pro-pseudocatalase PC-KUS reduced social anxiety/avoidance, anxious-depressive mood and depression. Patients without low-key stigmatisation scored highest in DLQI and social anxiety/avoidance. Avoidance and concealing behavior correlated with all questionnaires' scores.
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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 | 0.003 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.000 | 0.000 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.000 | 0.000 |
| Open science | 0.000 | 0.000 |
| Research integrity | 0.000 | 0.000 |
| 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".