Risk of aortic aneurysm in patients with psoriasis: A systematic review and meta‐analysis of cohort studies
Bibliographic record
Abstract
Abstract Background The association between psoriasis and the risk of aortic aneurysm is still unclear. Hypothesis Patients with psoriasis have a higher risk of aortic aneurysm than healthy individuals. Methods PubMed, Embase, and Scopus from inception to 20 July 2019 were searched. We included cohort studies if they reported estimate effects on the risk of aortic aneurysm in patient with psoriasis. We used Newcastle‐Ottawa Scale to evaluate methodology quality of eligible studies. Random‐effect meta‐analyses were used to estimate the overall risk. Subgroup analyses were conducted for analysis of influencing factors. Results After a view of 2207 citations, we included three large cohort studies enrolling 5 706 525 participants in this systematic review. Psoriasis patients have an increased risk of development of aortic aneurysm (hazard ratio [HR]: 1.30, 95%confidence intervals [CI], 1.10‐1.55, I 2 = 53.1%). The risk is not statistically different between patients with severe psoriasis (HR, 1.51, 95%CI, 1.04‐2.19, I 2 = 40.2%) and patients with mild psoriasis (HR, 1.24, 95%CI, 1.08‐1.42, I 2 = 24.1%). The risk was not statistically increased in female patients (HR, 1.55, 95%CI, 0.65‐3.72), patients ≥50 years old (HR, 4.05, 95%CI, 0.69‐23.75, I 2 = 97.3%), and patients with diabetes (HR, 0.97, 95%CI, 0.83‐1.14). Conclusions Current evidence from observational studies suggests that psoriasis increases the risk of aortic aneurysm, and screening of aortic aneurysm might be considered among psoriasis patients.
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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 | 0.011 | 0.028 |
| Meta-epidemiology (narrow) | 0.002 | 0.001 |
| Meta-epidemiology (broad) | 0.014 | 0.029 |
| Bibliometrics | 0.006 | 0.007 |
| Science and technology studies | 0.001 | 0.001 |
| Scholarly communication | 0.003 | 0.001 |
| Open science | 0.002 | 0.001 |
| Research integrity | 0.002 | 0.002 |
| Insufficient payload (model declined to judge) | 0.003 | 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".