American College of Rheumatology/EULAR remission criteria for rheumatoid arthritis: 2022 revision
Bibliographic record
Abstract
OBJECTIVE: In 2011, the American College of Rheumatology (ACR) and EULAR endorsed provisional criteria for remission in rheumatoid arthritis (RA), both Boolean-based and index-based. Based on recent studies indicating that a higher threshold for the patient global assessment (PtGA) may improve agreement between the two sets of criteria, our goals were to externally validate a revision of the Boolean remission criteria using a higher PtGA threshold and to validate the provisionally endorsed index-based criteria. METHODS: We used data from four randomised trials comparing biological disease-modifying antirheumatic drugs to methotrexate or placebo. We tested the higher proposed PtGA threshold of 2 cm (Boolean2.0) (range 0-10 cm) compared with the original threshold of 1 cm (Boolean1.0). We analysed agreement between the Boolean-based and index-based criteria (Simplified Disease Activity Index (SDAI) and Clinical Disease Activity Index (CDAI)) for remission and examined how well each remission definition predicted later good physical function (Health Assessment Questionnaire (HAQ) score≤0.5) and radiographic non-progression. RESULTS: Data from 2048 trial participants, 1101 with early RA and 947 with established RA, were included. The proportion of patients with disease in remission at 6 months after treatment initiation increased when using Boolean2.0 compared with Boolean1.0, from 14.8% to 20.6% in early RA and 4.2% to 6.0% in established RA. Agreement between Boolean2.0 and the SDAI or CDAI remission criteria was better than for Boolean1.0, particularly in early disease. Boolean2.0, SDAI, and CDAI remission criteria had similar positive likelihood ratios (LRs) to predict radiographic nonprogression and a HAQ score of ≤0.5 (positive LR 3.8-4.3). The omission of PtGA (BooleanX) worsened the prediction of good functional outcomes. CONCLUSION: Using the Boolean 2.0 criteria classifies, more patients as achieving remission and increases the agreement with index-based remission criteria without jeopardising predictive value for radiographic or functional outcomes. This revised Boolean definition and the previously provisionally endorsed index-based criteria were endorsed by ACR and EULAR.
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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 | 0.008 | 0.013 |
| Meta-epidemiology (narrow) | 0.003 | 0.001 |
| Meta-epidemiology (broad) | 0.003 | 0.004 |
| Bibliometrics | 0.008 | 0.004 |
| Science and technology studies | 0.001 | 0.001 |
| Scholarly communication | 0.002 | 0.002 |
| Open science | 0.003 | 0.002 |
| Research integrity | 0.003 | 0.005 |
| Insufficient payload (model declined to judge) | 0.012 | 0.011 |
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".