Risk of Mortality of People With Psoriasis and Psoriatic Arthritis in Taiwan: A Nationwide Cohort Study
Bibliographic record
Abstract
OBJECTIVE: Residual confounding effects and disease severity are attributed to controversial results in studies of psoriatic disease (PsD) and mortality. We aimed to evaluate the risk of mortality in patients with incident PsD, compared to matched controls from the population. METHODS: We used the nationwide, population-based insurance claim datasets in Taiwan from 2010 to 2018. Incident cases of PsD were identified by International Classification of Diseases (ICD) codes. A nonexposed cohort was established through propensity score matching (PSM). Deaths were identified via the National Mortality Database. We evaluated the risk of all-cause mortality in PsD compared to the PSM nonexposed individuals using Cox regression. The mortality risk was evaluated in patients with more severe disease stratified by systemic therapy use and having psoriatic arthritis (PsA). RESULTS: < 0.001). After PSM, we found an attenuated but persistent higher risk of mortality in PsD compared to controls (aHR 1.20, 95% CI 1.16-1.24). There was a trend of higher mortality in patients exposed to biologic therapies, but not for PsA. CONCLUSION: There was an increased risk of all-cause mortality in individuals with PsD compared to individuals without PsD before and after both PSM and adjustment for comorbidities. The risk of mortality was higher in patients with psoriasis but not in patients with PsA as compared to controls.
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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 | 0.001 |
| 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 | 0.000 | 0.000 |
| Open science | 0.000 | 0.000 |
| Research integrity | 0.000 | 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".