Evaluation of the symbol digit modalities test (SDMT) and MS neuropsychological screening questionnaire (MSNQ) in natalizumab-treated MS patients over 48 weeks
Bibliographic record
Abstract
BACKGROUND AND OBJECTIVES: Brief cognitive tests to monitor cognitive impairment in patients with multiple sclerosis (MS) are needed. METHODS: Performance on monthly administrations of the Symbol Digit Modalities Test (SDMT) and the MS Neuropsychological Questionnaire (MSNQ) was assessed in 660 patients with MS in 21 countries (109 sites) for 48 weeks in an open-label, safety-extension study of natalizumab. RESULTS: At baseline, the cohort's mean age was 40.1 years, 67.6% were female and the median Expanded Disability Status Scale score was 2.5. Test-retest correlations were high for both SDMT (range 0.89 for weeks 0-4 to 0.96 for weeks 44-48) and MSNQ (0.82 for weeks 0-4 to 0.93 for weeks 44-48). There were no statistically significant effects of geographic region. While SDMT scores improved by 15 points over 48 weeks (p < 0.0001), incremental monthly changes were small (effect size d < 0.3). Similar results were obtained on the MSNQ except that scores moved downward, suggesting fewer cognitive complaints over 48 weeks (p < 0.0001), but again the incremental monthly changes were small (d <-0.2). CONCLUSIONS: These results replicate earlier work in a smaller cohort treated with conventional disease-modifying therapy, and support the reliability of the SDMT and MSNQ as potential screening for monitoring tools for cognition over time.
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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 | 0.004 |
| Meta-epidemiology (narrow) | 0.001 | 0.000 |
| Meta-epidemiology (broad) | 0.001 | 0.001 |
| Bibliometrics | 0.001 | 0.001 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.000 | 0.001 |
| Open science | 0.000 | 0.000 |
| Research integrity | 0.000 | 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".