Increased Prevalence of Systemic Lupus Erythematosus Comorbidity in Patients With Psoriatic Arthritis: A Population-based Case-control Study
Bibliographic record
Abstract
Objective. To assess the prevalence of systemic lupus erythematosus (SLE) in a psoriatic arthritis (PsA) cohort and to compare it to the general population using the database of a large healthcare provider. Methods. We analyzed the database of a PsA cohort (2002–2017), matched for age and sex, with randomly selected controls for demographics, clinical and laboratory manifestations, and dispensed medications. Statistical analysis used t test and chi-square test as appropriate. In the PsA group, incidence density sampling was performed matching PsA patients without SLE as controls to each case of PsA with SLE by age and follow-up time. Univariable and multivariable conditional logistic regression analyses were used to assess factors affecting SLE development. Results. The PsA and control groups consisted of 4836 and 24,180 subjects, respectively, with a median age of 56 ± 15 years, and of whom 53.8% were female. Eighteen patients (0.37%) in the PsA group and 36 patients (0.15%) in the control group were diagnosed with SLE (P = 0.001). SLE patients without PsA had higher anti-dsDNA and anticardiolipin antibodies. The usage of drugs with known potential to induce SLE was higher in the PsA than in the control group. Older age at PsA diagnosis, shorter PsA duration, and statin treatment were associated with SLE in PsA patients. Conclusion. A 2.3-fold increase in the prevalence of SLE in PsA relative to the control group was found. Risk factors for SLE development included older age at PsA diagnosis, shorter PsA duration, and statin treatment. The association between PsA and SLE may affect treatment choices and medication development.
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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 | 0.002 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.001 | 0.001 |
| Bibliometrics | 0.001 | 0.001 |
| Science and technology studies | 0.001 | 0.000 |
| Scholarly communication | 0.001 | 0.000 |
| Open science | 0.000 | 0.000 |
| Research integrity | 0.001 | 0.001 |
| Insufficient payload (model declined to judge) | 0.002 | 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".