Performance of Antinuclear Antibodies for Classifying Systemic Lupus Erythematosus: A Systematic Literature Review and Meta‐Regression of Diagnostic Data
Bibliographic record
Abstract
OBJECTIVE: To review the published literature on the performance of indirect immunofluorescence (IIF)-HEp-2 antinuclear antibody (ANA) testing for classification of systemic lupus erythematosus (SLE). METHODS: A systematic literature search was conducted in the Medline, Embase, and Cochrane databases for articles published between January 1990 and October 2015. The research question was structured according to Population, Intervention, Comparator, Outcome (PICO) format rules, and Preferred Reporting Items for Systematic Reviews and Meta-Analyses (PRISMA) recommendations were followed where appropriate. Meta-regression analysis for diagnostic tests was performed, using the ANA titer as independent variable, while sensitivity and specificity were dependent variables. RESULTS: Of 4,483 publications screened, 62 matched the eligibility criteria, and another 2 articles were identified through reference analysis. The included studies comprised 13,080 SLE patients in total, of whom 12,542 (95.9%) were reported to be IIF-ANA positive at various titers. For ANA at titers of 1:40, 1:80, 1:160, and 1:320, meta-regression gave sensitivity values of 98.4% (95% confidence interval [95% CI] 97.6-99.0%), 97.8% (95% CI 96.8-98.5%), 95.8% (95% CI 94.1-97.1%), and 86.0% (95% CI 77.0-91.9%), respectively. The corresponding specificities were 66.9% (95% CI 57.8-74.9%), 74.7% (95% CI 66.7-81.3%), 86.2% (95% CI 80.4-90.5%), and 96.6% (95% CI 93.9-98.1%), respectively. CONCLUSION: The results of this systematic literature review and meta-regression confirm that IIF-ANAs have high sensitivity for SLE. ANAs at a titer of 1:80 have sufficiently high sensitivity to be considered as an entry criterion for SLE classification criteria, i.e., formally test other classification criteria for SLE only if ANAs of at least 1:80 have been found.
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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 | 0.037 | 0.107 |
| Meta-epidemiology (narrow) | 0.005 | 0.002 |
| Meta-epidemiology (broad) | 0.024 | 0.052 |
| Bibliometrics | 0.013 | 0.011 |
| Science and technology studies | 0.001 | 0.001 |
| Scholarly communication | 0.004 | 0.003 |
| Open science | 0.004 | 0.002 |
| 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".