The Incidence and Prevalence of Uveitis in Psoriasis: A Systematic Review and Meta-Analysis
Bibliographic record
Abstract
BACKGROUND: The systemic effects and comorbidities of psoriasis include ocular disorders, such as uveitis. Patients with psoriatic arthritis in particular have been demonstrated to have an elevated risk for developing uveitis. Presently, the risk of uveitis in psoriasis has yet to be fully elucidated and this systematic review seeks to address this gap. OBJECTIVE: To examine the prevalence and incidence of uveitis in psoriasis patients compared to non-psoriasis patients. METHODS: We conducted a systematic review search on MEDLINE, Embase, and CENTRAL electronic databases with no lower limit on year of publication. RESULTS: Fourteen articles met our inclusion criteria, with a total of 234 143 psoriasis subjects. Two studies found that participants with severe psoriasis were at a greater risk of uveitis than those with mild psoriasis. A random-effects meta-analysis of the 3 studies, which reported risk of incidence of uveitis in psoriasis patients compared to non-psoriasis controls, found a pooled risk ratio of 1.29 (95% CI, 1.10-1.51), indicating an increased risk of uveitis in psoriasis. Three studies compared risk of uveitis in psoriatic arthritis with psoriasis-only participants, all finding that psoriatic arthritis was associated with a greater risk of uveitis. CONCLUSIONS: In summary, our findings suggest that psoriasis is associated with an increased risk of uveitis, with or without psoriatic arthritis.
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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 | 0.007 | 0.018 |
| Meta-epidemiology (narrow) | 0.002 | 0.001 |
| Meta-epidemiology (broad) | 0.013 | 0.021 |
| Bibliometrics | 0.006 | 0.007 |
| Science and technology studies | 0.001 | 0.001 |
| Scholarly communication | 0.002 | 0.001 |
| Open science | 0.002 | 0.001 |
| Research integrity | 0.002 | 0.001 |
| Insufficient payload (model declined to judge) | 0.004 | 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".