Psychological Aspects of Cutaneous Pain in Psoriasis
Bibliographic record
Abstract
Introduction: Psoriasis is a chronic inflammatory disease that negatively impacts patients’ quality of life (QoL) and mental health. Itch and pain are prevalent symptoms of psoriasis and contribute to the psychosocial burden of this disease. This study aimed to evaluate the impact of skin pain on the prevalence and severity of symptoms of anxiety and depression and on the QoL in psoriasis patients. Methods: The studied population comprised 106 adults with psoriasis (34% female; mean age 42.1 ± 13.0 years). Disease severity was measured with the Psoriasis Area and Severity Index (PASI). The intensity of skin pain was assessed with the NRS and the Short Form McGill Pain Questionnaire (SF-MPQ). The Generalized Anxiety Disorder-7 (GAD-7) and Patient Health Questionnaire-9 (PHQ-9) questionnaires were used to estimate the severity of depression and anxiety, respectively, as was the Hospital Anxiety and Depression Scale (HADS). Quality of life (QoL) was studied using the Dermatology Life Quality Index (DLQI). Results: Regarding anxiety assessment, females reported significantly higher scores with the HADS-A (8.42 ± 4.85 points vs. 5.14 ± 3.9 points; p < 0.001) and the GAD-7 compared to men (7.50 ± 5.58 points vs. 5.24 ± 4.79 points; p = 0.036). Similarly, the severity of depression was significantly higher in women, as measured with the PHQ-9 (7.50 ± 5.58 points vs. 5.24 ± 4.79 points, p = 0.021). Psoriasis patients with skin pain scored significantly higher in HADS Total score (p = 0.043), HADS-A (p = 0.022), PHQ-9 (p = 0.035), and DLQI (p < 0.001) than the rest of the studied group. The intensity of skin pain measured with the SF-MPQ correlated significantly with HADS Total score (p = 0.021), HADS-A (p < 0.001), HADS-D (p = 0.038), and PHQ-9 (p < 0.001). Additionally, there was a significant correlation between the intensity of cutaneous pain assessed using the VAS and the PHQ-9 (p = 0.022). Conclusions: Skin pain significantly influences the well-being of patients with psoriasis as well as the symptoms of anxiety and depression. In particular, women with psoriasis are at increased risk of developing anxiety and depression. Our findings underline the necessity for a multidisciplinary approach to the management of this dermatosis.
Fetched live from OpenAlex and de-inverted. Abstracts are not stored in this database: the inverted indexes are 8.6 GB of the frame’s 9.3 GB of text, and the host has 13 GB free.
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 | 0.001 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.000 | 0.000 |
| Open science | 0.000 | 0.000 |
| Research integrity | 0.000 | 0.001 |
| 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".