Relationship between patient acceptable symptom state and disease scores in psoriasis
Bibliographic record
Abstract
Patient acceptable symptom state (PASS) is a patient-reported outcome that reflects patients' perspective well. The relationship between the PASS and disease scores in psoriasis has not been described. The aim of the present study was to investigate the association of PASS with Psoriasis Area and Severity Index (PASI) and body surface area (BSA) affected by lesions in patients with psoriasis. A sectional study was conducted. PASS was evaluated by a binary question on the patient's feeling that they have about their symptoms. Clinical data including PASI, BSA, and other patient characteristics were collected. Logistic regression was used to investigate the associations. Receiver-operator curve (ROC) analysis was utilized to determine the PASI/BSA thresholds for PASS. A total of 198 participants (27.8% female, mean age 41.9 ± 12.6 years, mean disease duration 10.2 ± 8.6 years) completed this study. Of patients with mild psoriasis, 71.4% based on PASI and 76.3% based on BSA considered their symptom state acceptable. Female sex (adjusted odds ratio [OR] = 0.47; 95% confidence interval [CI = 0.42-0.92) and patients with exposed skin involved (adjusted OR = 0.38; 95% CI = 0.19-0.76) were less likely to report acceptable symptom state. The threshold for differentiating psoriasis patients in PASS was 3.85 (area under the curve [AUC], 0.67; sensitivity, 0.67; specificity, 0.60) for PASI and 2.85% (AUC, 0.66; sensitivity, 0.79; specificity, 0.54) for BSA, respectively. These results showed that mild psoriasis based on PASI/BSA score align well with PASS status. Female and exposed skin involved are risk factors for acceptable status. Both PASI and BSA have limited capability in differentiating acceptable symptom state in psoriasis.
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 | 0.002 | 0.008 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.001 |
| Bibliometrics | 0.001 | 0.001 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.001 | 0.000 |
| Open science | 0.000 | 0.000 |
| Research integrity | 0.000 | 0.001 |
| Insufficient payload (model declined to judge) | 0.001 | 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".