The Incidence and Predictors of Infection in Psoriasis and Psoriatic Arthritis: Results from Longitudinal Observational Cohorts
Bibliographic record
Abstract
OBJECTIVE: To investigate the rate, type, characteristics, and predictors of infection in a cohort of patients with psoriatic arthritis (PsA) and a cohort of patients with psoriasis without arthritis (PsC). METHODS: A cohort of patients with PsA and a cohort of patients with PsC were followed according to a standard protocol and information on the occurrence of infections was recorded. The rate of infection was estimated by fitting an exponential model. A Weibull regression model was fitted to estimate the relative risk of first infection associated with a number of covariates. Risk factors for recurrent infections were investigated using generalized estimating equations. RESULTS: There were 498 and 74 infections reported among 695 and 509 patients with PsA and PsC, respectively, with an incidence rate of 19.6 per 100 person-years in the PsA cohort compared with 12.2 in the PsC cohort. The HR of the time to the first infection in PsA versus PsC was 1.6 (p = 0.002), and higher in patients treated with biologics versus nonbiologics at 1.56 (95% CI 1.22-2.00) in PsA and 1.50 (95% CI 0.64-3.54) in the PsC cohorts. Female sex and treatment with biologics were associated with infection in the PsA cohort, whereas a lower Psoriasis Area and Severity Index score and a higher Functional Comorbidity Index were associated with infection in the PsC cohort. Ultraviolet treatment was protective against infection in both cohorts. No difference in rates of hospitalization was found (p = 0.66). There were no infection-related deaths in either cohort. CONCLUSION: The incidence rate of infection was higher in the PsA than the PsC cohort and higher among patients treated with biologics. The data confirm the association between infection and biologic treatment in psoriatic disease.
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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 | 0.006 | 0.015 |
| 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 | 0.001 |
| Open science | 0.001 | 0.001 |
| 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".