Comparison of Alexithymia, General Health and Coping Strategies between Patients with Dermatologic Diseases and Control Group
Bibliographic record
Abstract
Background: People, who can identify, understand and express their inner feelings well, cope with negative experiences and adapt with environment and other people successfully. Alexithymia is a personality trait characterized by difficulty in identifying, describing and regulating feelings. Patients with alexithymia, who cannot cope with stress, somatize their emotional disturbance in a variety of medical disorders, particularly dermatologic diseases. Methods: In this prospective case-control study, 63 eligible subjects referring to dermatology clinics of Isfahan University of Medical sciences (Isfahan, Iran) were selected by convenience sampling method and implied case group. Control group was selected from normal population without any skin problem. The General health questionnaire (GHQ), Toronto alexithymia scale (TAS-20) and Coping response index (CRI) were used to compare the variables between the groups. Findings: Alexithymia had association with dermatologic diseases while CRI and GHQ scores showed no significant difference between the groups of case and control. Conclusion: Present research suggests that in the treatment of dermatologic diseases, psychological problems should be considered. Dermatologist should be aware of alexithymia association with an underlying dermatologic disease.
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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 | 0.001 |
| 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) | 0.003 | 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".