Meaningful Change in Cognition in Multiple Sclerosis: Method Matters
Bibliographic record
Abstract
OBJECTIVE: To determine if different methods of evaluating cognitive change over time yield measurably different outcomes. METHODS: Twelve cognitively impaired patients with clinically definite Multiple sclerosis (10 relapsing-remitting, 2 secondary progressive) underwent neuropsychological testing (baseline, 6, 12 months). Data was analysed using: t-tests evaluating group differences on individual tests, group differences in composite scores, reliable change analyses at the level of the individual, and comparisons regarding number of tests failed at each time point. RESULTS: Group t-tests on individual tests yielded no change. When tests were grouped according to theoretical constructs, analyses revealed change in processing speed. Reliable change estimates revealed that 16% of the sample deteriorated. When change was measured with respect to the number of domains affected at each time point, 58% of the sample deteriorated on at least one subtest. CONCLUSIONS: Methodology has a significant impact on interpretation of longitudinal data. In the same group of subjects, traditional group analyses documented no change in individual test scores or change on a single composite score. Analyses of individual results documented change from 16 to 58% of the sample. Advantages and disadvantages of each method were discussed. Findings have implications for interpretation of longitudinal studies.
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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 | 0.235 | 0.453 |
| Meta-epidemiology (narrow) | 0.001 | 0.001 |
| Meta-epidemiology (broad) | 0.002 | 0.002 |
| Bibliometrics | 0.003 | 0.005 |
| Science and technology studies | 0.001 | 0.004 |
| Scholarly communication | 0.003 | 0.002 |
| Open science | 0.003 | 0.003 |
| Research integrity | 0.001 | 0.002 |
| Insufficient payload (model declined to judge) | 0.003 | 0.001 |
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; the direct Gemma label and the distilled Codex classifier agree on what is shown here.
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".