Diagnostic Value of Anti-Sa and Anticitrullinated Protein Antibodies in Rheumatoid Arthritis
Bibliographic record
Abstract
OBJECTIVE: To determine the diagnostic value of anticitrullinated protein antibodies, second generation (ACPA2), by electrochemiluminescent immunoassay (ECLIA) and anti-Sa by ELISA in a large cohort of Chinese patients with early rheumatoid arthritis (RA). METHODS: One hundred ninety-eight patients with early RA (< 1 yr duration), 112 with other rheumatic diseases, and 60 healthy individuals were studied. RESULTS: The combination of anti-Sa and ACPA2 positivity had the highest specificity (99.42%), but it had a rather low sensitivity (50.0%). The combination of anti-rheumatoid factor (RF) and ACPA2 showed the highest sensitivity (80.30%), with specificity of 95.93%. The mean titer of ACPA2 and RF was significantly higher in the anti-Sa-positive group compared to the negative group (ACPA2, p <0.001; RF, p = 0.007). The 28-joint Disease Activity Scores of the anti-Sa-positive patients were significantly higher than those of the negative group (p = 0.01). The anti-Sa had no significant correlation with age, sex, antinuclear antibody, SSA, SSB, erythrocyte sedimentation rate, C-reactive protein, immunoglobulin A (IgA), IgG, IgM, C3, and C4. CONCLUSION: Our results come from a newly developed ECLIA for detection of ACPA2 and the anti-Sa-antibody-based ELISA system. The combined application of ACPA2 and anti-Sa tests can improve the laboratory diagnosis of early RA.
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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 | 0.003 |
| Meta-epidemiology (narrow) | 0.001 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.001 | 0.000 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.000 | 0.000 |
| Open science | 0.000 | 0.000 |
| Research integrity | 0.001 | 0.000 |
| Insufficient payload (model declined to judge) | 0.001 | 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".