Changes Over Time in the Diagnosis of Rheumatoid Arthritis in a 10-year Cohort
Bibliographic record
Abstract
OBJECTIVE: We assessed levels of agreement between a diagnosis of rheumatoid arthritis (RA) at inclusion in a recent-onset arthritis cohort, then 2 and 10 years later. Performance of American College of Rheumatology (ACR) criteria alone or combined with rheumatologist diagnosis, and of recent new criteria adding antibodies to cyclic citrullinated peptides ("anti-CCP-revised criteria") to existing ACR criteria, was evaluated. METHODS: In total, 270 patients with recent-onset arthritis of less than 1 year duration were included between 1995 and 1997 and followed for 2 years. A diagnosis was recorded by an office-based rheumatologist (OBR) at inclusion, then 2 years later. In 2007, a questionnaire was sent to each rheumatologist to collect the final diagnosis, which was considered the reference. RESULTS: Final diagnosis was available for 164 patients: 57 had RA. Agreement was low (kappa = 0.27) between the baseline and final diagnoses, and substantial (kappa = 0.69) between the 2-year and final diagnoses. Anti-CCP-revised criteria had sensitivity of 65% to 81% and specificity of 55% to 75%. Sensitivity and specificity of ACR criteria were 57.9% (44.1%-70.9%) and 74.8% (65.5%-82.7%) at inclusion, 80.7% (70.5%-90.0%) and 63.6% (54.5%-72.7%) at 2 years. The combination OBR diagnosis/ACR criteria after 2 years showed considerably increased specificity (87% vs 64%) and slightly decreased sensitivity (77% vs 81%). CONCLUSION: ACR criteria for RA showed poor performance even at 2 years. The absence of exclusion criteria may explain the lack of specificity, which improved when combined with the OBR diagnosis. Adding anti-CCP criteria to the existing criteria could help in diagnosing 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 | 0.003 | 0.010 |
| Meta-epidemiology (narrow) | 0.000 | 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 | 0.001 |
| 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".