Clinical Response Within 12 Weeks as a Predictor of Future Low Disease Activity in Patients with Early RA: Results from the TEAR Trial
Bibliographic record
Abstract
OBJECTIVE: Rapidly predicting future outcomes based on short-term clinical response would be helpful to optimize rheumatoid arthritis (RA) management in early disease. Our aim was to derive and validate a clinical prediction rule to predict low disease activity (LDA) at 1 year among patients participating in the Treatment of Early Aggressive Rheumatoid Arthritis (TEAR) trial escalating RA therapy by adding either etanercept or sulfasalazine + hydroxychloroquine [triple therapy (TT)] after 6 months of methotrexate (MTX) therapy. METHODS: Eligible subjects included in the derivation cohort (used for model building, n = 186) were participants with moderate or higher disease activity [Disease Activity Score 28-erythrocyte sedimentation rate (DAS-ESR) > 3.2] despite 24 weeks of MTX monotherapy who added either etanercept or sulfasalazine + hydroxychloroquine. Clinical characteristics measured within the next 12 weeks were used to predict LDA 1 year later using multivariable logistic regression. Validation was performed in the cohort of TEAR patients randomized to initially receive either MTX + etanercept or TT. RESULTS: The derivation cohort yielded 3 prediction models of varying complexity that included age, DAS28 at various timepoints, body mass index, and ESR (area under the receiver-operator characteristic curve up to 0.83). Accuracy of the prediction models ranged between 80% and 95% in both derivation and validation cohorts, depending on the complexity of the model and the cutpoints chosen for response and nonresponse. About 80% of patients could be predicted to be responders or nonresponders at Week 12. CONCLUSION: Clinical data collected early after starting or escalating disease-modifying antirheumatic drug/biologic treatment could accurately predict LDA at 1 year in patients with early RA. For patients predicted to be nonresponders, treatment could be changed at 12 weeks to optimize outcomes.
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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 | 0.012 |
| Meta-epidemiology (narrow) | 0.001 | 0.000 |
| Meta-epidemiology (broad) | 0.001 | 0.002 |
| Bibliometrics | 0.000 | 0.000 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.001 | 0.001 |
| Open science | 0.001 | 0.001 |
| Research integrity | 0.001 | 0.002 |
| 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".