Anticentromere-A and Anticentromere-B Antibodies Show High Concordance and Similar Clinical Associations in Patients with Systemic Sclerosis
Bibliographic record
Abstract
OBJECTIVE: To determine the diagnostic sensitivity and specificity and the clinical usefulness of parallel anticentromere-A and anticentromere-B antibody (anti-CENP-A and anti-CENP-B) testing in patients with systemic sclerosis (SSc). METHODS: Sera from 280 consecutive patients with SSc and 259 controls were tested for the presence of anti-CENP-A and anti-CENP-B antibodies by a monospecific line immunoblot assay (LIA) with recombinant human centromere proteins A and B as well as by indirect immunofluorescence (IIF). Crossreactivity and possible associations with clinical manifestations were studied. RESULTS: Both antibodies revealed a diagnostic sensitivity of 36.8% and a specificity of > 97% for SSc, with a high concordance rate of 94.3% despite different amino acid sequences of the antigens and absence of crossreactivity. There was a significant correlation of the antibody levels measured by LIA. Both antibodies were associated with similar clinical manifestations and identified patients with limited disease and rather mild skin sclerosis. CONCLUSION: Detected by LIA, anti-CENP-A and anti-CENP-B antibodies show high concordance in patients with SSc and share significant associations to clinical manifestations, but are not completely identical. Detection of both antibodies in parallel may slightly increase the diagnostic sensitivity for SSc.
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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 | 0.001 | 0.004 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.001 | 0.001 |
| Science and technology studies | 0.000 | 0.001 |
| Scholarly communication | 0.000 | 0.000 |
| Open science | 0.000 | 0.000 |
| Research integrity | 0.001 | 0.000 |
| Insufficient payload (model declined to judge) | 0.002 | 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".