Prevalence of oral mucosal lesions in patients with systemic Lupus Erythematosus: a systematic review and meta-analysis
Bibliographic record
Abstract
BACKGROUND: Systemic lupus erythematosus (SLE) is a chronic autoimmune disease that can cause a range of symptoms, including oral mucosal lesions (OMLs). The prevalence of OMLs in SLE patients and their associated factors have been studied in various regions, but the results are inconsistent. This study aims to evaluate the prevalence of OMLs in patients with SLE. METHODS: Observational studies of OML prevalence in SLE patients published before 2022 were retrieved from PubMed, Embase, Web of Science, Google Scholar, and the Cochrane Library without language restriction. The quality of the studies was assessed using the Newcastle-Ottawa Scale (NOS) and Agency for Healthcare Research and Quality (AHRQ). RESULTS: Our meta-analysis included 113 studies with a total of 53,307 SLE patients. We found that the prevalence of OMLs in SLE patients was 31% (95% CI: 28%, 35%), with oral ulcers being present in 30% of SLE patients (95% CI: 26%, 33%). Subgroup analysis showed that the prevalence of OMLs varied significantly by region, disease activity, and sample size (p ≤ 0.01). However, gender and year of publication had little effect on the prevalence of OMLs (p = 0.78 and 0.30, respectively). Oral ulcers were significantly associated with age of onset (p = 0.02), geographic location (p < 0.01), and race (p < 0.01). We also found that the prevalence of oral erythema was 9%, oral candidiasis was 9%, petechiae was 8%, cheilitis was 6%, and white plaque was 3%. CONCLUSIONS: Our analysis showed that the prevalence of OMLs varied significantly by region and disease activity, and child-onset patients of Indian, Malay, and Caucasian descent were more likely to have oral ulcers. The high prevalence of OML in SLE patients emphasizes the importance of regular oral examination and management in the comprehensive care of individuals with SLE.
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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 | 0.002 |
| Meta-epidemiology (broad) | 0.016 | 0.034 |
| Bibliometrics | 0.007 | 0.009 |
| Science and technology studies | 0.001 | 0.001 |
| Scholarly communication | 0.003 | 0.002 |
| Open science | 0.002 | 0.001 |
| Research integrity | 0.002 | 0.001 |
| 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".