P.009 Long-term comparative efficacy of inebilizumab from N-MOmentum participants versus azathioprine and immunosuppressants and placebo in NMOSD patients
Bibliographic record
Abstract
Background: Long-term efficacy of inebilizumab (INEB), an anti-CD19+ B cell-depleting antibody approved for the treatment of seropositive-aquaporin-4-antibody (AQP4+) neuromyelitis optica spectrum disorder (NMOSD) was evaluated over N-MOmentum (NCT02200770) open-label period (OLP) vs azathioprine and other immunosuppressants (AZA/IST) and vs PBO. Methods: Two historical comparator groups (HCGs), AZA/IST (N=132) and PBO (N=106), derived from published NMOSD studies, were used to compare efficacy of INEB (N=208) over the OLP. Hazard ratios (HR) for INEB vs HCGs were estimated using Cox proportional hazards (PH) regression. Time to NMOSD attack was analysed using parametric and flexible survival (spline) models. Results: Time to NMOSD attack for N-MOmentum PBO compared to PBO was HR 1.15;(95% CI:0.67–1.91; P =0.58). The HRs for time to NMOSD attack for INEB vs AZA/IST and PBO groups were 0.29(95% CI:0.17, 0.42; P <0.001) and 0.15 (95% CI:0.10, 0.21; P <0.001). At 4 years, estimated attack-free survival was 77% (95% CI:71, 83) for INEB, 36% (95% CI:27, 46) for AZA/IST, and 12% (95% CI:7, 20) for PBO. Conclusions: INEB was associated with a statistically significant reduction in risk of an NMOSD attack and provided a long-term attack-free probability over the OLP compared to the relative short-term benefit observed with AZA/IST.
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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 | 0.003 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.001 | 0.001 |
| Bibliometrics | 0.000 | 0.000 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.000 | 0.001 |
| Open science | 0.000 | 0.000 |
| Research integrity | 0.000 | 0.001 |
| Insufficient payload (model declined to judge) | 0.009 | 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".