Lung function preservation in a phase 3 trial of tocilizumab (TCZ) in systemic sclerosis (SSc)
Bibliographic record
Abstract
Background: Anti–interleukin-6 receptor antibody TCZ showed clinically relevant lung function preservation (forced vital capacity [FVC]) in SSc patients (pts) in a phase 2 trial. Objective: Investigate TCZ vs placebo (PBO) in SSc in a phase 3 trial. Methods: Pts were randomized 1:1 to double-blind subcutaneous TCZ 162 mg or PBO per week (wk) for 48 wks. Primary endpoint: difference (TCZ vs PBO) in modified Rodnan skin score (mRSS) change from baseline (∆BL). Secondary endpoints: percent-predicted (pp)FVC; time to treatment failure (TTF; time from first study treatment to first occurrence of death, FVC decline >10%, mRSS increase ≥20% and mRSS ≥5, or occurrence of SSc complications); Health Assessment Questionnaire–Disability Index (HAQ-DI); pt/physician global assessment (Pt/PhGA) visual analog scale. Results: Of 106 PBO and 104 TCZ pts, 31% had previous/current interstitial lung disease based on their history. At wk 48, adjusted least-squares mean difference ∆BL mRSS PBO vs TCZ was –1.7 [95% CI: –3.8, 0.3], p=0.098). Cumulative distribution of ∆BL ppFVC (median [IQR] PBO –3.9 [–7.2, 0.6]; TCZ –0.6 [–5.3, 3.9] van Elteren nominal p=0.0015) and difference in mean ∆BL FVC at wk 48 (167 mL [95% CI: 83, 250]) favored TCZ. TTF hazard ratio (95% CI) was 0.6 (0.4, 1.1) numerically favoring TCZ (Cox proportional hazards p=0.082). No clinically meaningful difference was seen in mean (95% CI) difference ∆BL HAQ-DI –0.1 (–0.2, 0.1), PtGA –2.4 (–8.6, 3.7), PhGA –2.5 (–8.7, 3.8). Safety profile was consistent with SSc complications and TCZ treatment. Conclusion: The primary mRSS endpoint was not met. A clinically relevant difference in FVC was seen for TCZ vs PBO, with preservation of lung function.
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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 | 0.002 | 0.001 |
| Meta-epidemiology (narrow) | 0.001 | 0.000 |
| Meta-epidemiology (broad) | 0.001 | 0.001 |
| Bibliometrics | 0.000 | 0.000 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.001 | 0.000 |
| Open science | 0.000 | 0.000 |
| Research integrity | 0.001 | 0.002 |
| Insufficient payload (model declined to judge) | 0.003 | 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".