Repeated Anticitrullinated Protein Antibody and Rheumatoid Factor Assessment Is Not Necessary in Early Arthritis: Results from the ESPOIR Cohort
Bibliographic record
Abstract
OBJECTIVE: Presence and levels of anticyclic citrullinated peptide antibodies (anti-CCP) and rheumatoid factor (RF) contribute to the classification and prognosis of rheumatoid arthritis (RA). The objective was to determine the usefulness of repeating anti-CCP/RF measurements during the first 2 years of followup in patients with early arthritis. METHODS: In patients with early undifferentiated arthritis, serial anti-CCP and RF were measured using automated second-generation assays every 6 months for 2 years. Frequencies of seroconversions (from negative to positive or the reverse) and changes in antibody levels during followup were determined. RESULTS: In all, 775 patients, mean (SD) age 48.2 (12.5) years, mean symptom duration 3.4 (1.7) months, 76.6% female, were analyzed; 614 (79.2%) satisfied the American College of Rheumatology/European League Against Rheumatism 2010 classification criteria for RA at baseline. At baseline, respectively for anti-CCP and RF, 318 (41.0%) and 181 (23.4%) patients were positive, of whom 298 (93.7% of the positive) and 111 (61.3% of the positive) were highly positive (above 3 × upper limit of the norm). There were only 12 anti-CCP seroconversions toward the positive (i.e., 2.6% of the anti-CCP-negative), 21 seroconversions toward the negative (6.6% of the anti-CCP-positive), and 8 (1.0%) changes to a higher anti-CCP level category during the 2-year followup; respectively for RF, 27 (4.6%), 95 (52.5%), and 13 (1.7%). CONCLUSION: In this cohort of patients with early arthritis, including in the subset of patients who did not fulfill the RA criteria, antibody status showed little increase over a 2-year period. Repeated measurements of anti-CCP/RF very infrequently offer significant additional information.
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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 | 0.004 |
| Meta-epidemiology (narrow) | 0.001 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.001 |
| Bibliometrics | 0.001 | 0.001 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.001 | 0.001 |
| Open science | 0.001 | 0.001 |
| Research integrity | 0.001 | 0.000 |
| Insufficient payload (model declined to judge) | 0.002 | 0.001 |
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".