Assessing the validity of a computer-generated cognitive screening instrument for patients with multiple sclerosis
Bibliographic record
Abstract
BACKGROUND: Neuropsychological testing requires considerable time, expense, and expertise to administer. These factors can limit patient access. Computerized cognitive testing has been proposed as an alternative. OBJECTIVES: The objective of this paper is to validate a brief, simple-to-use computer-generated cognitive assessment screening battery for multiple sclerosis (MS) patients that has minimal motor involvement. METHODS: A sample of 96 MS patients and 98 healthy controls completed a computer-generated battery that included the Stroop, Symbol Digit Modalities Test (C-SDMT), a two- and four-second visual analog of the Paced Auditory Serial Addition Test (PVSAT-2, PVSAT-4), and simple and choice reaction time tests. The Minimal Assessment of Cognitive Function in MS was used to define cognitive impairment in the MS sample. RESULTS: Each newly developed test successfully distinguished between cognitively impaired patients and healthy controls as well as cognitively intact patients. A combination of three computerized tests (C-SDMT, PVSAT-2, PVSAT-4) with a mean administration time of 10 minutes had a sensitivity of 82.5% and specificity of 87.5% in detecting cognitive impairment. Good test-retest reliability was obtained for each measure. CONCLUSIONS: Good sensitivity and specificity, brevity, ease of administration, and a limited motor component highlight the feasibility of introducing this computer-generated cognitive screening instrument in a busy MS clinic.
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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 | 0.003 | 0.018 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.001 | 0.001 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.001 | 0.001 |
| Open science | 0.000 | 0.001 |
| Research integrity | 0.001 | 0.001 |
| Insufficient payload (model declined to judge) | 0.002 | 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; 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".