Computerized neuropsychological assessment devices in multiple sclerosis: A systematic review
Bibliographic record
Abstract
BACKGROUND: The proliferation of computerized neuropsychological assessment devices (CNADs) for screening and monitoring cognitive impairment is increasing exponentially. Previous reviews of computerized tests for multiple sclerosis (MS) were primarily qualitative and did not rigorously compare CNADs on psychometric properties. OBJECTIVE: We aimed to systematically review the literature on the use of CNADs in MS and identify test batteries and single tests with good evidence for reliability and validity. METHOD: A search of four major online databases was conducted for publications related to computerized testing and MS. Test-retest reliability and validity coefficients and effect sizes were recorded for each CNAD test, along with administration characteristics. RESULTS: We identified 11 batteries and 33 individual tests from 120 peer-reviewed articles meeting the inclusion criteria. CNADs with the strongest psychometric support include the CogState Brief Battery, Cognitive Drug Research Battery, NeuroTrax, CNS-Vital Signs, and computer-based administrations of the Symbol Digit Modalities Test. CONCLUSION: We identified several CNADs that are valid to screen for MS-related cognitive impairment, or to supplement full, conventional neuropsychological assessment. The necessity of testing with a technician, and in a controlled clinic/laboratory environment, remains uncertain.
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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 | 0.004 | 0.022 |
| Meta-epidemiology (narrow) | 0.001 | 0.001 |
| Meta-epidemiology (broad) | 0.005 | 0.004 |
| Bibliometrics | 0.011 | 0.011 |
| Science and technology studies | 0.000 | 0.001 |
| Scholarly communication | 0.002 | 0.002 |
| Open science | 0.002 | 0.001 |
| Research integrity | 0.001 | 0.001 |
| Insufficient payload (model declined to judge) | 0.004 | 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".