Do Psoriasis and Atopic Dermatitis Affect Memory, Attention, Stress and Emotions?
Bibliographic record
Abstract
BACKGROUND: Psoriasis and atopic dermatitis are chronic skin diseases found all over the world that cause a lot of suffering to patients. OBJECTIVES: The aim of this study was to answer the following questions: whether people suffering from psoriasis and AD have greater problems with recognizing emotions, the effectiveness of attention and memory processes, and whether they use different strategies of coping with stress than healthy people. METHODS: This study involved 90 patients, including 30 patients with psoriasis, 30 patients with AD and 30 healthy patients, aged 21 to 63 years, including 54 women and 36 men. This study used a battery of the CANTAB Cognitive Tests, Mini-COPE Questionnaire Inventory, Toronto Alexithymia Scale TAS Questionnaire, Psoriasis Area and Severity Index, and Eczema Area and Severity Index. RESULTS: People with psoriasis and AD had higher total scores on the alexithymia scale and had greater difficulty in identifying and verbalizing emotions. People with psoriasis and AD are less likely to choose the correct stimulus and achieve a shorter length of the sequence that should be remembered. Psoriasis patients with more severe symptoms are less likely to use the strategy of a sense of humor in stressful situations. AD patients with more severe symptoms are less likely to use strategies of operative thinking, denial and self-blame, and the strategy of seeking instrumental support is used more often. CONCLUSIONS: Patients with psoriasis and AD require a holistic approach; in addition to dermatological treatment, psychological support, psychotherapeutic support and possible psychiatric treatment are recommended.
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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 | 0.000 | 0.000 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.001 | 0.000 |
| Open science | 0.000 | 0.000 |
| Research integrity | 0.001 | 0.000 |
| Insufficient payload (model declined to judge) | 0.004 | 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".