Anti-cyclic citrullinated peptide antibody as a marker of erosive arthritis in patients with systemic lupus erythematosus: a systematic review and meta-analysis
Bibliographic record
Abstract
OBJECTIVE: Anti-cyclic citrullinated peptide (CCP) antibody is an established marker in the diagnosis and prognostication of rheumatoid arthritis (RA). Infrequently, systemic lupus erythematosus (SLE) patients also develop a deforming erosive arthritis, similar to that of RA. Our objective was to determine whether anti-CCP antibody is a useful marker of erosive disease in SLE patients presenting with arthritis. METHODS: Electronic databases EMBASE, MEDLINE and non-indexed MEDLINE citations were searched through April 11, 2014, using the outlined key terms. Studies meeting predefined inclusion and exclusion criteria were reviewed. Two reviewers independently assessed the quality of included articles using previously described criteria. The DerSimonian-Laird random effects model was used to calculate pooled sensitivity and specificity of anti-CCP antibody for erosive arthritis in SLE. RESULTS: Seven articles met inclusion and exclusion criteria. A total of 609 SLE patients with arthritis were identified, 70 of whom had erosive disease. Pooled sensitivity and specificity of anti-CCP antibody for erosive arthritis was 47.8% (95% CI, 26.2%-70.2%) and 91.8% (95% CI, 78.4%-97.2%), respectively. CONCLUSION: Our findings suggest that anti-CCP antibody is a highly specific marker for erosive arthritis in SLE. Longitudinal prospective studies are needed to determine if anti-CCP antibody can be used as a predictor of erosive disease.
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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 | 0.027 |
| Meta-epidemiology (narrow) | 0.002 | 0.002 |
| Meta-epidemiology (broad) | 0.021 | 0.030 |
| Bibliometrics | 0.008 | 0.010 |
| Science and technology studies | 0.001 | 0.001 |
| Scholarly communication | 0.003 | 0.002 |
| Open science | 0.002 | 0.001 |
| Research integrity | 0.003 | 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".