Timing and Magnitude of Initial Change in Disease Activity Score 28 Predicts the Likelihood of Achieving Low Disease Activity at 1 Year in Rheumatoid Arthritis Patients Treated with Certolizumab Pegol: A Post-hoc Analysis of the RAPID 1 Trial
Bibliographic record
Abstract
OBJECTIVE: To determine the relationship between timing and magnitude of Disease Activity Score [DAS28(ESR)] nonresponse (DAS28 improvement thresholds not reached) during the first 12 weeks of treatment with certolizumab pegol (CZP) plus methotrexate, and the likelihood of achieving low disease activity (LDA) at 1 year in patients with rheumatoid arthritis. METHODS: In a post-hoc analysis of the RAPID 1 study, patients achieving LDA [DAS28(ESR) ≤ 3.2] at Year 1 were assessed according to DAS28 nonresponse at various timepoints within the first 12 weeks. RESULTS: Seven-hundred eighty-three patients were included (CZP 200 mg, n = 393; CZP 400 mg, n = 390). A total of 86.9% of patients in the CZP 200 mg group had a DAS28 improvement of ≥ 1.2 by Week 12. Of the 13.1% of patients with DAS28 improvement < 1.2 by Week 12, only 2.0% had LDA at Year 1. Failure to achieve LDA at Year 1 depended on timing of nonresponse - 22.3%, 8.4%, and 2.0% of patients with DAS28 improvement < 1.2 by Weeks 1, 6, and 12, respectively, had LDA at Year 1 - and magnitude of initial lack of DAS28 improvement; for example, compared with the patients with DAS28 < 1.2 improvement, fewer patients with DAS28 < 0.6 had LDA at Year 1 (17.4%, 2.4%, and 0.0% at Weeks 1, 6, and 12, respectively). CONCLUSION: Failure to achieve improvement in DAS28 within the first 12 weeks of therapy was predictive of a low probability of achieving LDA at Year 1. Moreover, the accuracy of the prediction was found to be strongly dependent on the magnitude and timing of the lack of the response. (Clinical Trial Registration Nos. NCT00152386 and NCT00175877).
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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 | 0.006 | 0.005 |
| Meta-epidemiology (narrow) | 0.001 | 0.000 |
| Meta-epidemiology (broad) | 0.002 | 0.003 |
| Bibliometrics | 0.000 | 0.000 |
| Science and technology studies | 0.000 | 0.001 |
| Scholarly communication | 0.001 | 0.001 |
| Open science | 0.001 | 0.001 |
| Research integrity | 0.001 | 0.002 |
| Insufficient payload (model declined to judge) | 0.002 | 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".