The association of serum anti-ribosomal P antibody with clinical and serological disorders in systemic lupus erythematosus: a systematic review and meta-analysis
Bibliographic record
Abstract
OBJECTIVE: Anti-ribosomal P (anti-P) antibody is a serological specific marker of systemic lupus erythematosus (SLE). The aim of this study is to investigate the association of this antibody with clinical and serological disorders in SLE. METHODS: All relevant literature was retrieved from PubMed, EMBASE, Web of Science and CNKI databases. The qualities of these studies were evaluated using a modified version of the Newcastle-Ottawa scale. The associations of anti-P antibody with clinical and serological disorders were determined by the pooled odds ratio (OR) and the confidence interval (CI) calculated using meta-analysis with the Mantel-Haenszel method. RESULTS: Sixteen cohort studies with 2355 patients were included in this study. Malar rash, oral ulcer and photosensitivity were strongly associated with serum anti-P antibody, with OR (95% CI) values of 2.05 (1.42-2.92), 1.49 (1.05-2.13) and 1.44 (1.08-1.91), respectively. Arthritis and renal involvement were not associated with anti-P antibody, whereas a high heterogeneity was observed due to ethnicity and publication bias, respectively. Neuropsychiatric SLE (NPSLE), hepatic involvement, anti-dsDNA, anti-Sm and anti-cardiolipin antibodies (aCL) were observed more frequently in anti-P positive patients than in negative patients. Studies on hepatic involvement showed a low precision with substantially broad CI (2.56-11.2). A high heterogeneity presented among studies on NPSLE, anti-Sm and aCL. CONCLUSIONS: Anti-P antibody is significantly associated with malar rash, oral ulcer, photosensitivity and serum anti-dsDNA antibody, and potentially associated with NPSLE, hepatic damage, serum anti-Sm and aCL.
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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 | 0.008 | 0.019 |
| Meta-epidemiology (narrow) | 0.002 | 0.001 |
| Meta-epidemiology (broad) | 0.017 | 0.030 |
| Bibliometrics | 0.008 | 0.010 |
| Science and technology studies | 0.001 | 0.001 |
| Scholarly communication | 0.003 | 0.001 |
| Open science | 0.002 | 0.001 |
| Research integrity | 0.002 | 0.001 |
| 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".