Lack of seroconversion of rheumatoid factor and anti-cyclic citrullinated peptide in patients with early inflammatory arthritis: a systematic literature review
Bibliographic record
Abstract
OBJECTIVE: Serological markers are thought to be useful in predicting which patients with early inflammatory arthritis (EIA) will progress to RA. The objective of this study is to determine the per cent RF and anti-CCP seroconversion in EIA patients at 1-5 years of follow-up: 80% of established RA is RF or CCP positive. METHODS: We conducted a systematic literature review of all English publications and recent abstracts from ACR and EULAR. Patients ≥16 years of age with at least one swollen joint and symptoms < 2 years were included. RESULTS: Twelve publications met the criteria: 10 studies included data on RF, while only 5 addressed anti-CCP. Sample sizes ranged from 15 to 395 and follow-up was 6-60 months. There was marked heterogeneity between studies; therefore, results could not be pooled for a meta-analysis. Baseline RF and anti-CCP positivity was also highly variable: 8-55 and 4-45%, respectively. Seroconversion rates for EIA were 1.9-5.0% at up to 30 months follow-up for RF and 1.3-8.9% at up to 60 months follow-up for anti-CCP. CONCLUSION: There is minimal change in RF or anti-CCP positivity up to 5 years of follow-up. Prevalence data for RF in established RA is significantly higher than the baseline values reported here. The low rates of seroconversion would suggest a lower prevalence in EIA and the reason for this difference remains unknown. It is unclear whether antibody-negative patients are more likely to remit and be lost to follow-up in established RA populations.
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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 | 0.010 | 0.039 |
| Meta-epidemiology (narrow) | 0.001 | 0.001 |
| Meta-epidemiology (broad) | 0.009 | 0.009 |
| Bibliometrics | 0.011 | 0.011 |
| Science and technology studies | 0.001 | 0.001 |
| Scholarly communication | 0.003 | 0.002 |
| Open science | 0.002 | 0.001 |
| Research integrity | 0.002 | 0.001 |
| Insufficient payload (model declined to judge) | 0.003 | 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".