Clinical Characteristics of Cutaneous Pain in Psoriasis
Bibliographic record
Abstract
Background: Psoriasis is a common inflammatory disease that is often associated with itch and pain. This study aimed to evaluate the clinical characteristics of skin pain among patients with psoriasis. Materials: A total of 106 patients diagnosed with psoriasis were included in the study (34% female; mean age 42.1 ± 13.0 years). Disease severity was assessed using the Psoriasis Area and Severity Index (PASI). Itch severity was evaluated using the numeric rating scale (NRS) and 4-Item Itch Score (4IIS). The intensity of skin pain was measured through the NRS, short-form McGill pain questionnaire (SF-MPQ), visual analog scale (VAS), and Douleur Neuropathique-4 questionnaire (DN4). Results: In the past week, 84.9% of psoriasis patients reported itch, while 50% of them reported skin pain. The average NRS for itch was 4.52 ± 2.88 points, and the 4IIS yielded a mean score of 6.79 ± 4.37 points. In terms of the intensity of cutaneous pain, the mean NRS was 2.42 ± 2.96 points; the SF-MPQ score averaged 4.84 ± 7.51 points; and the VAS score was 1.92 ± 2.65 points. Furthermore, 17% of adult psoriasis patients reported neuropathic pain. In 84.9% of the participants, skin pain was concurrent with areas affected by itch, while 18.9% of patients exhibited cutaneous pain encompassing all itchy areas. The pain NRS demonstrated significant correlations with the SF-MPQ (r = 0.531, p < 0.001), VAS (r = 0.779, p < 0.001), itch NRS (r = 0.551, p < 0.001), and 4IIS (r = 0.569, p < 0.001). No association was found between the pain NRS and PASI or disease duration. Conclusions: Skin pain of mild intensity and itch of moderate intensity are prevalent symptoms in psoriasis patients. Strong correlations between skin pain and itch can be explained by the process of neurogenic inflammation.
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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 | 0.003 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.001 | 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 | 0.000 |
| 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".