Increased Risk of Systemic Lupus Erythematosus in Patients with Chronic Urticaria: A Systematic Review and Meta-analysis
Bibliographic record
Abstract
Introduction: The association between systemic lupus erythematosus (SLE) and chronic urticaria (CU) has been suggested in the literature although the amount of evidence is still relatively limited. We aimed to combine all available studies on this association using systematic review and meta-analysis technique. Methods: Potentially eligible studies were identified from Medline and EMBASE from inception to February 2023 using search strategy that comprised of terms for "chronic urticaria" and "systemic lupus erythematosus". The eligible study must consist of one group of patients with CU and another group of comparators without CU and must compare the prevalence of SLE in each group and report effect size with 95% confidence intervals (95% CIs). We extracted such data from each study to calculate a pooled odds ratio using the generic inverse variance method with random-effect model. Funnel plot was used to evaluate publication bias. Newcastle-Ottawa Scale was used to appraise the methodological quality of the included studies. Results: A total of 5,155 articles were identified. After two rounds of independent review by four investigators, five studies met the eligibility criteria and were included in the meta-analysis. The meta-analysis found an increased prevalence of SLE among patients with CU compared with individuals without CU with the pooled odds ratio of 5.03 (95% CI, 2.57-9.85, I2 of 93%). Conclusion: This systematic review and meta-analysis found that patients with CU had a significantly increased risk of SLE compared to individuals without CU.
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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 | 0.014 | 0.031 |
| Meta-epidemiology (narrow) | 0.003 | 0.002 |
| Meta-epidemiology (broad) | 0.020 | 0.041 |
| Bibliometrics | 0.009 | 0.009 |
| Science and technology studies | 0.001 | 0.001 |
| Scholarly communication | 0.004 | 0.002 |
| Open science | 0.002 | 0.001 |
| Research integrity | 0.003 | 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".