The Association of Psoriatic Arthritis With All-cause Mortality and Leading Causes of Death in Psoriatic Arthritis
Bibliographic record
Abstract
OBJECTIVE: To examine the association between psoriatic arthritis (PsA) and all-cause mortality from a large population-based database. METHODS: Patients with PsA from the Clalit Health Services database were identified between 2003-2018 and matched to 4 controls by age, sex, ethnicity, and index date. Patient demographics, comorbidities, and treatments were extracted. Mortality data were obtained from the Israeli Notification of Death certificate. The proportionate mortality rate (PMR) of the leading causes of death was calculated and compared to that of the general population. Cox proportional hazard regression models were used to estimate the crude and the multivariate adjusted HR for the association between PsA and all-cause mortality and for factors associated with mortality within the PsA group. RESULTS: There were 5275 patients with PsA and 21,011 controls included and followed for 7.2 ± 4.4 years. The mean age was 51.7 ± 15.4 years, and 53% were females. Among patients with PsA, 38.2% were on biologics. Four hundred seventy-one (8.9%) patients died in the PsA group compared to 1668 (7.9%) in the control group. The crude HR for the association of PsA and all-cause mortality was 1.16 (95% CI 1.04-1.29) and 1.02 (95% CI 0.90-1.15) on multivariate analysis. Malignancy was the leading cause of death (26%), followed by ischemic heart disease (15.8%); this is in keeping with the leading causes of death in the general population. Older age, male sex, lower socioeconomic status, increased BMI, increased Charlson comorbidity index scores, and history of psoriasis or hospitalization in 1 year prior to entry were positive predictors for mortality. CONCLUSION: No clinically relevant increase in mortality rate was observed in patients with PsA, and specific PMRs were similar to those of the general population.
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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 | 0.001 |
| 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 | 0.000 |
| 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".