The relationship between alopecia areata and alexithymia, anxiety and depression: A case-control study
Bibliographic record
Abstract
BACKGROUND: Alopecia areata (AA) is a skin disease characterized by the sudden appearance of areas of hair loss on the scalp and other hair-bearing areas, but its aesthetic repercussions can lead to profound changes in patient's psychological status and relationships. AIM: The goal was to investigate a possible relationship between AA and alexithymia as well as two other emotional dimensions, anxiety and depression. MATERIALS AND METHODS: Fifty patients with AA seen in the Department of Dermatology of Hedi Chaker University Hospital, Sfax were included in this study. Anxiety and depression were evaluated by Hospital Anxiety and Depression scale questionnaire, alexithymia was assessed by Toronto Alexithymia scale 20, and severity of AA was measured by Severity of Alopecia Tool. RESULTS: Patient's mean age was 32.92 years. 52% of patients were females. Depression and anxiety were detected respectively in 38% and 62% of patients. There was statistically significant difference between patients and control group in terms of depression (P = 0.047) and anxiety (P = 0.005). Forty-two percent of patients scored positive for alexithymia. No significant difference was found between patient and control groups (P = 0.683) in terms of alexithymia. Anxiety was responsible for 14.7% of variation in alexithymia (P = 0.047). CONCLUSIONS: Our study shows a high prevalence of anxiety and depressive symptoms in AA patients. Dermatologists should be aware of the psychological impact of AA, especially as current treatments have limited effectiveness.
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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) | 0.000 | 0.001 |
| Meta-epidemiology (broad) | 0.001 | 0.001 |
| Bibliometrics | 0.001 | 0.001 |
| Science and technology studies | 0.001 | 0.001 |
| Scholarly communication | 0.001 | 0.001 |
| Open science | 0.000 | 0.000 |
| Research integrity | 0.001 | 0.001 |
| Insufficient payload (model declined to judge) | 0.003 | 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".