Anticitrullinated Protein Antibodies and Rheumatoid Factor Fluctuate in Early Inflammatory Arthritis and Do Not Predict Clinical Outcomes
Bibliographic record
Abstract
OBJECTIVE: In inflammatory arthritis, rheumatoid factor (RF) and anticitrullinated protein antibodies (ACPA) are believed to be associated with more severe clinical outcomes. Our objective was to determine whether ACPA and RF remain stable in early inflammatory arthritis and whether their trajectories over time or baseline levels predicted clinical outcomes. METHODS: The study population consisted of patients enrolled in the Canadian Early Arthritis Cohort Study with baseline and at least 12-month followup values of RF and ACPA. Primary outcomes were Disease Activity Score (DAS) remission and the presence of erosions at 12 and 24 months. Other objectives included swollen joint count, Health Assessment Questionnaire score, and DAS. RESULTS: At baseline, 225/342 (66%) patients were ACPA-positive and 334/520 (64%) were RF-positive. At 24 months, 15/181 (8%) ACPA-positive patients became negative. A larger number of patients changed from ACPA-negative to positive: 13/123 (11%). For RF, fluctuations were more common: 67/240 (28%) reverted from positive to negative and 21/136 (18%) converted from negative to positive. RF and ACPA fluctuations did not predict disease outcomes. Patients who remained ACPA-positive throughout followup were more likely to have erosive disease (OR 3.86, 95% CI 1.68, 8.92). CONCLUSION: RF and ACPA have the potential to revert and convert during the early course of disease. Fluctuations in RF and ACPA were not associated with clinical outcomes.
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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 | 0.003 | 0.009 |
| Meta-epidemiology (narrow) | 0.001 | 0.000 |
| Meta-epidemiology (broad) | 0.001 | 0.001 |
| Bibliometrics | 0.001 | 0.001 |
| Science and technology studies | 0.001 | 0.001 |
| Scholarly communication | 0.001 | 0.001 |
| Open science | 0.001 | 0.001 |
| Research integrity | 0.001 | 0.001 |
| 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".