Association Between Comorbidities and Quality of Life in Psoriatic Arthritis: Results from a Multicentric Cross-sectional Study
Bibliographic record
Abstract
Objective. In psoriatic arthritis (PsA), comorbidities add to the burden of disease, which may lead to poorer quality of life. The purpose of this study was to evaluate the relationship between comorbidities and quality of life (QOL). Methods. Patients from a multicentric, cross-sectional study on comorbidities in PsA were included in the analysis. Data on comorbidities were collected and were subsequently used to compute the modified Rheumatic Disease Comorbidity Index (mRDCI). The Medical Outcomes Study Short Form-36 questionnaire physical (PCS) and mental component summary (MCS) scales were used to assess QOL. Results. In total, 124 recruited patients fulfilled the ClASsification for Psoriatic ARthritis criteria (CASPAR): 62.1% were male; mean age and mean disease duration were 52.6 ± 12.6 years and 11.3 ± 9.6 years, respectively. The number of comorbid conditions was 2.0 ± 1.3, with 30.6% of the sample having currently or a history of 3 or more comorbidities. In the multivariate linear regression analysis, only anxiety remained significantly related to mental health (p < 0.0001). Anxiety alone accounted for 28.7% of the variance in MCS scores. Moreover, MCS was also significantly associated with the mRDCI score, which explained 4.9% of the variance in MCS [β = −1.56 (standard error 0.64), R2= 0.049, p = 0.0167]. In contrast, PCS was not significantly associated either with type or number of comorbidities. Conclusion. In this study, the type of comorbidity appeared to have a greater effect than the number of comorbidities. Indeed, anxiety in PsA was independently associated with QOL and would thus be an important factor to take into account in daily clinical practice.
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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 | 0.003 | 0.004 |
| 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 | 0.001 |
| Research integrity | 0.000 | 0.000 |
| 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".