The Toronto Observational Study of Natalizumab in Multiple Sclerosis
Bibliographic record
Abstract
BACKGROUND: Natalizumab is indicated for the treatment of relapsing multiple sclerosis (MS) with insufficient response to first-line disease-modifying therapy (DMT). We studied the efficacy of natalizumab for treatment of MS in a single centre observational design. METHODS: A retrospective observational study of 146 patients [66% female; mean age 37.4; 72% relapsing remitting MS (RRMS), 28% secondary progressive MS (SPMS)] referred for natalizumab treatment at St. Michael's Hospital MS Clinic between 2007 and August 2009. Data included demographic, clinical (Expanded Disability Status Scale (EDSS) and annualized relapse rate (ARR)) and patient self-report measures. RESULTS: The mean duration of treatment was 20 months in those treated with natalizumab and 97% had received prior DMTs. Eighty-three patients (57%) received at least 12 months of natalizumab treatment. In those who received at least 12 months of treatment, baseline ARR and EDSS were 1.6 and 2.7 in RRMS patients versus 1.0 and 5.4 in SPMS with relapses. The ARR decreased with natalizumab treatment to 0.38 (76% reduction, p<0.001) in RRMS versus 0.32 in SPMS patients (68% reduction, p=0.01). There was a treatment associated 11% reduction in EDSS to 2.4 (p=0.04) in RRMS, but no significant change in SPMS. Eighty-five percent of patients reported improved overall quality of life (QOL) and 62% indicated improved energy. CONCLUSIONS: There was a major reduction in relapse rate, stabilization in EDSS and improvement in QOL and energy in some patients on natalizumab, all similar to treatment effects in the pivotal trial.
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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 | 0.006 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.001 | 0.001 |
| Bibliometrics | 0.001 | 0.002 |
| Science and technology studies | 0.001 | 0.000 |
| Scholarly communication | 0.001 | 0.000 |
| Open science | 0.001 | 0.001 |
| 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".