Prediction of Remission in a French Early Arthritis Cohort by RAPID3 and other Core Data Set Measures, but Not by the Absence of Rheumatoid Factor, Anticitrullinated Protein Antibodies, or Radiographic Erosions
Bibliographic record
Abstract
OBJECTIVE: To identify baseline variables that predict remission according to different criteria in rheumatoid arthritis (RA) in a comprehensive French ESPOIR early arthritis database. METHODS: Individual variables and indices at baseline were analyzed in 664 patients for capacity to predict remission either 6 or 12 months later according to 4 criteria that require a formal joint count: the American College of Rheumatology/European League Against Rheumatism Boolean criteria, the Simplified Disease Activity Index, the Clinical Disease Activity Index, and the 28-joint Disease Activity Score; and 2 remission criteria that do not require a formal joint count: the Routine Assessment of Patient Index Data 3 (RAPID3) and the RAPID3 ≤ 3 + swollen joint, using univariate and multivariate logistic regressions. RESULTS: Remission was predicted significantly 6 and/or 12 months later in 26.8%-51.4% of patients, according to all 6 criteria by younger age, low index scores, and better status for the 6/7 clinical RA core dataset measures: tender joint count, swollen joint count (SJC), physician's global estimate, patient self-report Health Assessment Questionnaire (HAQ) physical function, pain, and patient's global estimate. Remission was not predicted by the absence of "poor prognosis RA" indicators, rheumatoid factor (RF), anticitrullinated protein antibodies (ACPA), or radiographic erosions. In multivariate regressions that included only 3 variables, low HAQ function predicted remission by all criteria as effectively as SJC, erythrocyte sedimentation rate, or C-reactive protein. CONCLUSION: Younger age and 6 core dataset clinical measures, but not the absence of traditional "poor prognosis RA" indicators, RF, ACPA, or radiographic erosions, predicted remission according to 6 criteria, including 2 without a formal joint count.
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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) | 0.000 | 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 | 0.000 |
| Open science | 0.000 | 0.000 |
| Research integrity | 0.000 | 0.000 |
| Insufficient payload (model declined to judge) | 0.002 | 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".