Cognitive test performance and disease progression in primary and secondary progressive MS: An analysis of the SPRINT-MS study
Bibliographic record
Abstract
BACKGROUND: Clinical trials of disease-modifying therapies (DMTs) for multiple sclerosis (MS) increasingly incorporate cognitive outcomes, although effects of DMTs on cognition remain unknown. METHODS: In this secondary analysis of data from SPRINT-MS, a phase 2 randomized, placebo-controlled trial of ibudilast for progressive MS, we evaluated performance on Symbol Digit Modalities Test (SDMT) and Selective Reminding Test (SRT) in relation to physical disability, brain volume assessed with magnetic resonance imaging, and retinal nerve fiber layer (RNFL) thickness over 96-week follow-up. We hypothesize that trial participants would show a decline in cognitive test scores over 96 weeks of follow-up, with a possible between-group difference in favor of ibudilast. RESULTS: Data from 255 participants were analyzed. On average, physical outcome measures, brain parenchymal fraction, and RNFL thickness worsened; average SDMT and SRT scores remained largely unchanged. There were no differences between treatment groups in cognitive outcomes at 96-week follow-up. Practice effects likely contributed to results. CONCLUSIONS: Observed stability of cognitive scores in individuals with progressive MS over 96-week follow-up may reflect true cognitive stability. However, the possibility that current cognitive measurement instruments are psychometrically flawed remains, warranting further research.
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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 | 0.007 | 0.007 |
| Meta-epidemiology (narrow) | 0.001 | 0.000 |
| Meta-epidemiology (broad) | 0.002 | 0.003 |
| Bibliometrics | 0.001 | 0.001 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.001 | 0.001 |
| Open science | 0.001 | 0.001 |
| Research integrity | 0.001 | 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".