Continued Benefit of Tocilizumab Plus Disease-modifying Antirheumatic Drug Therapy in Patients with Rheumatoid Arthritis and Inadequate Clinical Responses by Week 8 of Treatment
Bibliographic record
Abstract
OBJECTIVE: To evaluate whether patients with rheumatoid arthritis who did not respond sufficiently to tocilizumab (TCZ) plus disease-modifying antirheumatic drug (DMARD) treatment by Week 8 responded at later timepoints when continuing to take their original dose of TCZ. METHODS: In this posthoc analysis of data from phase III randomized controlled trials of inadequate responders (IR) to DMARD or tumor necrosis factor-α inhibitors (anti-TNF), percentages of patients meeting early response criteria were calculated by randomized treatment arm (TCZ 4 mg/kg, 8 mg/kg, or placebo in combination with DMARD). Percentages of patients achieving certain disease activity thresholds at later timepoints were calculated for patients who had/had not achieved response by Week 8. RESULTS: In DMARD-IR early nonresponders, 29.0%, 17.2%, and 3.7% of TCZ 8 mg/kg-randomized, TCZ 4 mg/kg-randomized, and placebo-randomized patients, respectively, achieved 28-joint Disease Activity Score (DAS28) ≤ 3.2 by Week 24. Among anti-TNF-IR patients without early response, 26.5%, 8.5%, and 1.9% of TCZ 8 mg/kg-randomized, TCZ 4 mg/kg-randomized, and placebo-randomized patients, respectively, achieved DAS28 ≤ 3.2 at Week 24. CONCLUSION: A substantial number of DMARD-IR patients taking TCZ 4 or 8 mg/kg and anti-TNF-IR patients taking TCZ 8 mg/kg who failed to respond by 8 weeks benefited from continued TCZ treatment in combination with DMARD. In contrast, the anti-TNF-IR patients without early responses who continued to take TCZ 4 mg/kg were unlikely to experience a cumulative benefit. ClinicalTrials.gov registration numbers: NCT00106548, NCT00106574, NCT00106535, NCT00106522.
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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 | 0.004 | 0.005 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.002 | 0.001 |
| Bibliometrics | 0.000 | 0.000 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.000 | 0.000 |
| Open science | 0.000 | 0.000 |
| 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".