Traumatic experiences, dissociative symptoms, and alexithymia in patients with alopecia areata
Bibliographic record
Abstract
Although genetic, environmental, autoimmune, and psychological factors are believed to play a role in the onset of alopecia areata (AA), the exact cause remains unknown. This study aimed to investigate whether there are differences in traumatic experiences, dissociative symptoms, and alexithymia levels between groups. Fifty eight patients diagnosed with AA, 58 individuals with dermatological diseases thought to have a low psychosomatic component, and 58 individuals not diagnosed with any chronic disease were included in the study. All participants were assessed using the Childhood Trauma Questionnaire (CTQ-28), Dissociative Experiences Scale (DES), Posttraumatic Stress Disorder Checklist for DSM-5 (PCL-5), and Toronto Alexithymia Scale (TAS-20). A Structured Clinical Interview for DSM-5 (SCID-5-CV) form was used to exclude additional psychiatric diagnoses. Mean scores on the CTQ-28 scale revealed differences between groups in terms of physical neglect and emotional neglect scores (p < 0.001; p = 0.022; p < 0.001). There were no differences in DES scores between groups (p = 0.085). When compared in terms of TAS-20 and PCL-5 scores, differences were found (p = 0.016; p = 0.024). As a result of this study, it was concluded that physical neglect and emotional neglect could play a significant role in the onset of AA. Alexithymia and traumatic stress disorder symptoms might be more prevalent in patients with AA.
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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 | 0.002 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.001 | 0.001 |
| Science and technology studies | 0.001 | 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".