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 distilled prediction
Teacher imitationNot calibrated prevalence, not ground truth. Human validation pending. Learned from the 10,348 direct Codex labels and 10,348 direct Gemma labels. Candidate is the union of thresholded teacher heads; consensus is their intersection. These outputs are machine_predicted_unvalidated and are not human labels or direct frontier model labels.
Codex and Gemma teacher scores by category
| Category | Codex | Gemma |
|---|---|---|
| Metaresearch | 0.001 | 0.002 |
| Meta-epidemiology (narrow) | 0.001 | 0.000 |
| Meta-epidemiology (broad) | 0.001 | 0.000 |
| Bibliometrics | 0.000 | 0.001 |
| Science and technology studies | 0.001 | 0.001 |
| Scholarly communication | 0.001 | 0.001 |
| Open science | 0.000 | 0.000 |
| Research integrity | 0.000 | 0.001 |
| Insufficient payload (model declined to judge) | 0.000 | 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 teacher head, 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".