Association of serum markers with improvement in clinical response measures after treatment with golimumab in patients with active rheumatoid arthritis despite receiving methotrexate: results from the GO-FORWARD study
Bibliographic record
Abstract
INTRODUCTION: The goal of this study was to identify serum markers that are modulated by treatment with golimumab with or without methotrexate (MTX) and are associated with clinical response. METHODS: Sera were collected at weeks 0 and 4 from a total of 336 patients (training dataset, n = 100; test dataset, n = 236) from the GO-FORWARD study of patients with active rheumatoid arthritis despite MTX. Patients were randomly assigned to receive placebo plus MTX; golimumab, 100 mg plus placebo; golimumab, 50 mg plus MTX; or golimumab, 100 mg plus MTX. Subcutaneous injections were administered every 4 weeks. Samples were tested for select inflammatory, bone, and cartilage markers and for protein profiling using multianalyte profiles. RESULTS: Treatment with golimumab with or without MTX resulted in significant decreases in a variety of serum proteins at week 4 as compared with placebo plus MTX. The American College of Rheumatology (ACR) 20, ACR 50, and Disease Activity Score (DAS) 28 responders showed a distinct biomarker profile compared with nonresponding patients. CONCLUSIONS: ACR 20 and ACR 50 responders among the golimumab/golimumab + MTX-treated patients had a distinct change from baseline to week 4 in serum protein profile as compared with nonresponders. Some of these changed markers were also associated with multiple clinical response measures and improvement in outcome measures in golimumab/golimumab + MTX-treated patients. Although the positive and negative predictive values of the panel of markers were modest, they were stronger than C-reactive protein alone in predicting clinical response to golimumab. TRIAL REGISTRATION: http://ClinicalTrials.gov identification number: NCT00264550.
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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 | 0.003 |
| Meta-epidemiology (narrow) | 0.001 | 0.000 |
| Meta-epidemiology (broad) | 0.001 | 0.001 |
| Bibliometrics | 0.000 | 0.001 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.001 | 0.000 |
| 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".