P-752 - Psychosomatic factors in alopecia areata
Bibliographic record
Abstract
Introduction Alopecia areata is a nonscarring hair disorder consisting in the sudden appearance of one or several circumscribed patches of hair loss. This often affects body image and carries a negative psychosocial impact for the patient. The pathogenesis of alopecia areata is not fully understood but psychosomatic factors such as emotional stress and specific personality traits have been suggested to play an important role in its development. Objectives and methods This study aims to understand the role of stressful events, attachment security, alexithymia and social support as factors triggering alopecia areata. Participants were recruited from a psychiatric out-patient clinic of a general hospital (n = 7) and were assessed using Paykel's Interview for Recent Life Events, Experiences in Close Relationships Scale, 20-item Toronto Alexithymia Scale and Social Support Satisfaction Scale. All scales were adapted to the Portuguese population. Results Alopecia areata tends to be associated with a low satisfaction in social support and high anxiety than avoidance in attachment relationships. Life events were important in 5 out of 7 patients studied and no association was found with alexithymia. Discussion/conclusion In our sample, poor social support, life events and anxious attachment were associated with Alopecia areata (in agreement with others previous studies), demonstrating the importance of psychosomatic factors in this disorder. Two patients that didn’t show a clear association with life events were under chronic stress situations (had mentally retarded children).
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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 | 0.000 |
| 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) | 0.008 | 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".