Examining the Relationship between Alopecia Areata, Androgenetic Alopecia, and Emotional Intelligence
Bibliographic record
Abstract
BACKGROUND: Emotional stress has been associated with the development of alopecia areata (AA) and androgenetic alopecia (AGA). Emotional intelligence (EI), a component of general intelligence, is thought to govern the recognition, expression, and control of stress and other emotions. People with low EI are unable to adequately control stress in everyday life. OBJECTIVE: To investigate EI differences between AA and AGA patients and a control population. METHODS: Thirty-five AGA patients and 42 AA patients, with patchy (n = 28), ophiasis (n = 5), totalis (n = 5), and universalis (n = 4) distribution of hair loss, completed a 133-item Emotional Quotient-Inventory (EQ-I ) psychometric assessment. Scores were compared between AA, AGA, and 77 control subjects obtained from the North American normative population sample on which the psychometric instrument was normed. RESULTS: Statistically significant differences were found in EI between AA patients and controls with the EQ-I Stress Tolerance scale (p = .005). AGA patients also differed significantly from the controls but to a lesser degree compared toAA patients. In overall EI, there were no apparent differences between AGA and AA patients. CONCLUSIONS: AA and AGA patients exhibit a mild depressive reaction to their condition, with AA patients demonstrating a significantly stronger deficiency in coping with stress than AGA patients. The data support a psychosomatic contribution to AA. Referral of patients for EI assessment and psychosocial counseling could help reduce stress.
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How this classification was reachedexpand
Full frame distilled prediction
Teacher imitationNot calibrated prevalence, not ground truth. Human validation pending. Learned from the 10,348 direct Codex labels and 10,348 direct Gemma labels. Candidate is the union of thresholded teacher heads; consensus is their intersection. These outputs are machine_predicted_unvalidated and are not human labels or direct frontier model labels.
Codex and Gemma teacher scores by category
| Category | Codex | Gemma |
|---|---|---|
| Metaresearch | 0.001 | 0.002 |
| 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) | 0.000 | 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 teacher head, 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".