Detecting cognitive dysfunction in a busy multiple sclerosis clinical setting: a computer generated approach
Bibliographic record
Abstract
BACKGROUND AND PURPOSE: This study aims to explore the effectiveness of a brief, computerized battery of tests in detecting cognitive differences between clinically isolated syndromes (CIS), relapsing-remitting multiple sclerosis (RRMS), primary progressive multiple sclerosis (PPMS) and secondary progressive multiple sclerosis (SPMS) patients. METHODS: Four groups of patients between the ages of 18 and 63 were enrolled from two hospital-based multiple sclerosis clinics: CIS (n = 42), RRMS (n = 44), PPMS (n = 15) and SPMS (n = 37). All subjects were administered a validated battery of five computerized cognitive tests: the STROOP Color-Word Test, the Computerized Symbol Digit Modalities Test, the Paced Visual Serial Addition Test (PVSAT) 4 s and 2 s trials, and a speed of cognition index obtained by subtracting simple reaction time from choice reaction time. Results were recorded by the test administrator. RESULTS: Significant between-group differences in cognition were evident on all tests (P < 0.01) with the exception of the PVSAT 2 s trial. CIS patients were the least impaired, SPMS the most. RRMS and PPMS patients generally had a similar cognitive profile, more impaired than the CIS patients but less so than the SPMS patients. These differences persisted after controlling for the effects of age and education. CONCLUSIONS: The ability of this computerized cognitive battery to distinguish the progression of cognitive deficits across the entire multiple sclerosis disease spectrum from CIS through to SPMS enhances its construct validity. This finding, coupled with the battery's brevity (20 min) and ease of administration, highlights its potential utility in a busy clinic setting.
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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 | 0.001 | 0.009 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.002 | 0.001 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.001 | 0.000 |
| Open science | 0.001 | 0.001 |
| Research integrity | 0.000 | 0.000 |
| Insufficient payload (model declined to judge) | 0.002 | 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".