Instruments measuring change in cognitive function in multiple sclerosis: A systematic review
Bibliographic record
Abstract
BACKGROUND: Multiple sclerosis (MS) is a chronic demyelinating/neurodegenerative disease associated with change in cognitive function (CF) over time. This systematic review aims to describe the instruments used to measure change in CF over time in people with MS (PwMS). METHODS: PubMed, OVID, Web of Science, and Scopus databases were searched in English until May 2021. Articles were included if they had at least 100 participants and at least a 1-year interval between baseline and last follow-up measurement of CF. Results were quantitatively synthesized, presented in tables and risk of bias was assessed with the Newcastle-Ottawa Scale. RESULTS: Fifty-seven articles met the inclusion criteria (41,623 PwMS and 1105 controls). An intervention (drug/rehabilitation) was assessed in 22 articles. In the studies that used a test battery, Visual and verbal learning and memory were the most frequently measured domains, but when studies that used test battery or a single test are combined, Information processing speed was the most measured. The Symbol Digit Modalities Test (SDMT) was the most frequently used test as a single test and in a test battery combined. Most studied assessed "change in CF" as cognitive decline defined as 1 or more tests measured as ≥ 1.5 SD from the study control or normative mean in a test battery at baseline and follow-up. Meta-analysis of change in SDMT scores with seven articles indicated a nonstatistically significant -0.03 (95% CI -0.14, 0.09) decrease in mean SDMT score per year. CONCLUSION: This study highlights the slow rate of measured change in cognition in PwMS and emphasizes the lack of a gold standard test and consistency in measuring cognitive change at the population level. More sensitive testing utilizing multiple domains and longer follow-up may define subgroups where CF change follows different trajectories thus allowing targeted interventions to directly support those where CF is at greatest risk of becoming a clinically meaningful issue.
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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 | 0.014 | 0.057 |
| Meta-epidemiology (narrow) | 0.002 | 0.001 |
| Meta-epidemiology (broad) | 0.013 | 0.014 |
| Bibliometrics | 0.011 | 0.012 |
| Science and technology studies | 0.001 | 0.001 |
| Scholarly communication | 0.003 | 0.002 |
| Open science | 0.002 | 0.002 |
| Research integrity | 0.002 | 0.001 |
| Insufficient payload (model declined to judge) | 0.003 | 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".