Relapse rates and enhancing lesions in a phase II trial of natalizumab in multiple sclerosis
Bibliographic record
Abstract
BACKGROUND: Natalizumab, a humanized monoclonal IgG4 antibody, is an alpha4-integrin antagonist, which inhibits the migration of inflammatory cells into the central nervous system, a key pathogenic mechanism in multiple sclerosis (MS). In a six month, phase II clinical trial of patients with relapsing MS, natalizumab significantly reduced the formation of new gadolinium-enhanced (Gd+) lesions and the number of clinical relapses. OBJECTIVE: To investigate the relationship of historical relapse rate and new Gd + lesion number with subsequent MS disease activity and natalizumab treatment in the phase II study. METHODS: Patients who participated in the phase II study were stratified into subgroups according to: (i) the number of relapses in the two years prior to entry into the study: 2 relapses (n = 108), 3 relapses (n =57), and >3 relapses (n =48); (ii) the number of new Gd + lesions at baseline (Month 0): 0 (n = 129), 1-2 (n =50), and >2 (n =33). Relapses and new Gd + lesions during the treatment phase of the trial were determined and compared for each subgroup. RESULTS: Both the prestudy relapse rate and number of new Gd + lesions at baseline were related to the subsequent risk of a relapse; baseline number of Gd + lesions was related to the likelihood of subsequent new Gd + lesion formation. There was a lower proportion of subjects with an on-study relapse and fewer new Gd + lesions in all natalizumab-treated subgroups when compared with their placebo counterpart; the difference was most apparent in the subgroups of patients with >3 relapses in the two years prior to study entry and >2 new Gd -- lesions at Month 0. CONCLUSIONS: There was a lower proportion of subjects with an on-study relapse in natalizumab-treated patients, particularly in those with a more active disease at study entry. Larger ongoing phase III studies will allow more definitive investigation of these preliminary subgroup findings.
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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 | 0.006 | 0.005 |
| 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 | 0.001 |
| Scholarly communication | 0.001 | 0.001 |
| Open science | 0.001 | 0.000 |
| Research integrity | 0.001 | 0.002 |
| 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".