Impact of Physician-Defined Flares on Quality of Life and Work Impairment: An International Survey of 2238 Patients With Psoriatic Arthritis
Bibliographic record
Abstract
Objective To describe psoriatic arthritis (PsA) flares and their effect on patient-reported outcomes (PROs). Methods Cross-sectional surveys of rheumatologists/dermatologists and their patients with PsA were conducted in France, Germany, Italy, Spain, the United Kingdom, and the United States, capturing data on physician-reported patient flare status, demographics, PsA severity, and clinical outcomes. Patient-completed surveys captured data on PROs: 5-level EuroQol 5-dimension, Work Productivity and Activity Impairment questionnaire, Health Assessment Questionnaire–Disability Index, and 12-item Psoriatic Arthritis Impact of Disease questionnaire. Patients were compared by flare status using parametric and nonparametric tests. Multivariate regression was used to identify flare associations. Multivariate logistic regression adjusted for patient demographics and physician specialty assessed the effect of flare status. Results Among 2238 patients (586 from the US, 1652 from Europe) managed by 572 physicians, physician-reported flare was present for 168 patients (7.5%), and self-reported flare was present for 95 patients (10% of available data). Mean (SD) flare count over 12 months was 2.2 (4.9), lasting on average 16.4 (16.2) days. Flare status was linked to worse PROs. Patients who had not flared in the last 12 months or had never flared had a higher quality of life, lower overall work impairment, and a lower degree of disability compared with patients who were currently experiencing a flare (all;P< 0.01). Conclusion Actively experiencing a flare adversely affected QOL, disability, and work productivity. PsA flares should be routinely assessed and managed in clinical care.
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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 | 0.001 | 0.004 |
| 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 | 0.001 | 0.001 |
| Open science | 0.000 | 0.000 |
| Research integrity | 0.001 | 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".