Cognitive evolution in natalizumab-treated multiple sclerosis patients
Bibliographic record
Abstract
BACKGROUND: Cognitive dysfunction affects up to 65% of multiple sclerosis (MS) patients and progresses over time. Natalizumab has been shown to be superior to placebo in preserving cognition for the first two years of therapy. OBJECTIVES: The objectives of this study are to understand the impact of natalizumab on cognition beyond two years of therapy and to investigate whether baseline characteristics are predictive of clinical response. METHODS: This is a single-center, 24-month, observational study. Sixty-three patients treated with natalizumab were assessed prior to monthly infusions using a Cogstate battery and the Symbol Digit Modalities Test (SDMT). Patient demographics were collected at baseline. A linear mixed model was conducted with duration of natalizumab therapy as a between-subjects factor (≤2 or >2 years), assessment as a within-subjects factor, and Multiple Sclerosis Severity Score (MSSS) as a covariate. RESULTS: = 0.0074), the two groups were identical. No patient showed evidence of sustained cognitive deterioration over the 24-month period. Baseline parameters including impaired cognition did not influence the trajectory of cognitive change over 24 months. CONCLUSIONS: Our results suggest that natalizumab preserves cognition following four to seven years of continuous therapy. This occurs irrespective of baseline characteristics, including impaired cognition.
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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 | 0.001 | 0.003 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.001 | 0.001 |
| Bibliometrics | 0.001 | 0.001 |
| Science and technology studies | 0.001 | 0.000 |
| Scholarly communication | 0.001 | 0.000 |
| Open science | 0.000 | 0.000 |
| Research integrity | 0.001 | 0.001 |
| 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".