Clinical effects of natalizumab on multiple sclerosis appear early in treatment course
Bibliographic record
Abstract
In clinical practice natalizumab is typically used in patients who have experienced breakthrough disease during treatment with interferon beta (IFNβ) or glatiramer acetate. In these patients it is important to reduce disease activity as quickly as possible. In a phase II study, differences between natalizumab and placebo in MRI outcomes reflecting inflammatory activity were evident after the first infusion and maintained through a 6-month period, suggesting a rapid onset of natalizumab treatment effects. To explore how soon after natalizumab initiation clinical effects become apparent, annualized relapse rates per 3-month period and time to first relapse were analyzed in the phase III AFFIRM study (natalizumab vs. placebo) and in the multinational Tysabri(®) Observational Program (TOP). In AFFIRM, natalizumab reduced the annualized relapse rate within 3 months of treatment initiation compared with placebo in the overall population (0.30 vs. 0.71; p < 0.0001) and in patients with highly active disease (0.30 vs. 0.94; p = 0.0039). The low annualized relapse rate was maintained throughout the 2-year study period, and the risk of relapse in AFFIRM patients treated with natalizumab was reduced [hazard ratio against placebo 0.42 (95 % CI 0.34-0.52); p < 0.0001]. Rapid reductions in annualized relapse rate also occurred in TOP (baseline 1.99 vs. 0-3 months 0.26; p < 0.0001). Natalizumab resulted in rapid, sustained reductions in disease activity in both AFFIRM and in clinical practice. This decrease in disease activity occurred within the first 3 months of treatment even in patients with more active disease.
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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 | 0.005 |
| 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 | 0.001 | 0.000 |
| Open science | 0.000 | 0.000 |
| Research integrity | 0.000 | 0.001 |
| 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".