Anti-carbamylated Protein Antibodies Are Present Prior to Rheumatoid Arthritis and Are Associated with Its Future Diagnosis
Bibliographic record
Abstract
OBJECTIVE: Anti-carbamylated protein (anti-CarP) antibodies could further elucidate early rheumatoid arthritis (RA) pathogenesis and predict clinical disease. We compared the diagnostic accuracy of anti-CarP antibodies for future RA to other RA-related antibodies in military personnel. METHODS: Stored pre-RA diagnosis serum samples from 76 RA cases were tested for anti-CarP fetal calf serum (FCS), anti-CarP fibrinogen (Fib), anticyclic citrullinated peptide antibodies version 2 (anti-CCP2), rheumatoid factor-nephelometry (RF-Neph), and RF isotypes [immunoglobulin M (IgM), IgG, and IgA]. Positivity for all antibodies was determined as ≥ 2 SD of log-transformed means from controls. Relationships between autoantibodies and future RA were assessed in prediagnosis serum for all RA cases compared to controls using sensitivity, specificity, and logistic regression. Differences in diagnostic accuracy between antibody combinations were assessed using comparisons of area under the curves (AUC). RESULTS: Anti-CarP-FCS was 26% sensitive and 95% specific for future RA, whereas anti-CarP-Fib was 16% sensitive and 95% specific for future RA. Anti-CarP-FCS positivity was associated with future RA, while anti-CarP-Fib trended toward association. The antibody combination of anti-CCP2 and/or ≥ 2 RF (RF-Neph and/or RF-isotypes) resulted in an AUC of 0.72 for future RA, where the AUC was 0.71 with the addition of anti-CarP-FCS to this prior combination. CONCLUSION: Adding anti-CarP-FCS to antibody combinations did not improve AUC. However, anti-CarP-FCS was associated with future onset of RA, and was present in prediagnosis serum in ∼10% of RA cases negative for anti-CCP2 but positive for RF.
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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 | 0.005 |
| Meta-epidemiology (narrow) | 0.001 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.001 | 0.001 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.001 | 0.000 |
| Open science | 0.000 | 0.000 |
| Research integrity | 0.001 | 0.001 |
| 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".