Diagnostic Accuracy of ACR/EULAR 2010 Criteria for Rheumatoid Arthritis in a 2-Year Cohort
Bibliographic record
Abstract
OBJECTIVE: To evaluate the diagnostic accuracy of the 2010 American College of Rheumatology/European League Against Rheumatism (ACR/EULAR) and 1987 ACR criteria for rheumatoid arthritis (RA), and the respective role of the algorithm and scoring of the ACR/EULAR. METHODS: In total, 270 patients with recent-onset arthritis of < 1 year duration were included prospectively between 1995 and 1997 and followed for 2 years. RA was defined as the combination, at completion of followup, of RA diagnosed by an office-based rheumatologist and treatment with a disease-modifying antirheumatic drug or glucocorticoid. We compared the sensitivity, specificity, positive predictive value (PPV), and negative predictive value (NPV) of the criteria sets in the overall population, in the subgroup meeting the tree condition for ACR/EULAR scoring, and in the overall population classified according the full tree. RESULTS: At baseline, 111 of the 270 patients had better alternative diagnoses and 16 had erosions typical for RA; of the 143 remaining patients, 52 had more than 6 ACR/EULAR 2010 points (indicating definite RA) and 91 had fewer than 6 points. After 2 years, 11/16 patients with erosions and 40/52 with more than 6 points had RA. 100 of the 270 patients met the reference standard for RA. Sensitivity, specificity, PPV, and NPV of the ACR/EULAR (full tree) were 51/100 (51%), 153/170 (90%), 51/68 (75.4%), and 153/202 (75.7%), respectively. Diagnostic accuracies of the ACR/EULAR score and ACR 1987 criteria were not statistically different. CONCLUSION: Much of the improvement of the ACR/EULAR criteria was ascribable to the use of exclusion criteria in the algorithm.
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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) | 0.001 | 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".