The Bidirectional Association Between Psoriatic Disease and Uveitis: An Updated Meta-Analysis
Bibliographic record
Abstract
BACKGROUND: In the past decade, there has been growing research interest in the association between psoriatic diseases and uveitis due to the potential pathogenetic links. However, observational studies have reported inconsistent results. METHODS: We thoroughly searched PubMed, Embase, and Web of Science for cohort or case-control studies investigating the bidirectional association between psoriatic diseases (psoriasis and psoriatic arthritis) and uveitis. The Newcastle-Ottawa Quality Assessment Scale was used for quality evaluation of included studies. Egger's test was used for publication bias assessment. We employed a random-effects model to pool individual data, using odds ratios (ORs) with 95% confidence interval (CI) as the effect measure. RESULTS: Eleven cohort studies and 1 case-control study with 9,641,856 participants were included. Eleven studies were considered as good quality, and 1 study was considered as fair quality. Egger's test indicated no significant publication bias. The meta-analysis indicated that patients with psoriatic disease was at higher risk of developing uveitis (OR: 2.14, 95% CI: 1.57-2.90). The result maintained consistent in patients with psoriatic arthritis (OR: 3.13, 95% CI: 2.10-4.67) but not in those with psoriasis without arthritis (OR: 1.40, 95% CI: 0.91-2.14). We also found that uveitis was associated with an increased risk of psoriatic disease (OR: 2.56, 95% CI: 1.66-3.96) including psoriasis without arthritis (OR: 1.99, 95% CI: 1.12-3.53) and psoriatic arthritis (OR: 3.53, 95% CI: 2.08-5.99). CONCLUSION: Our study suggested that uveitis was more likely to be bidirectionally associated with psoriatic arthritis rather than psoriasis without arthritis. More investigations focusing on the mechanisms are needed to better understand these findings from observational studies.
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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 | 0.016 | 0.029 |
| Meta-epidemiology (narrow) | 0.003 | 0.002 |
| Meta-epidemiology (broad) | 0.015 | 0.055 |
| Bibliometrics | 0.008 | 0.008 |
| Science and technology studies | 0.001 | 0.001 |
| Scholarly communication | 0.004 | 0.002 |
| Open science | 0.002 | 0.002 |
| Research integrity | 0.002 | 0.002 |
| 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".