Decision-making under explicit risk is impaired in multiple sclerosis: relationships with ventricular width and disease disability
Bibliographic record
Abstract
BACKGROUND: Decision-making is an essential function of everyday life. Decision-making under explicit risk requires developing advantageous decision strategies based on fixed outcomes (e.g., probabilities of winning or losing a bet). Decision-making and its neural substrates have been rarely studied in MS. We expected performance in decision-making under risk to be lowered in MS patients, and negatively correlated with disease-related disability, cognition, and ventricular width. METHODS: Three groups were included: 32 MS patients and 20 healthy controls were examined with conventional neuropsychological tests and the Game-of-Dice Task (GDT) assessing decision-making under explicit risk. Linear 2-D ventricular width was assessed on MS patients' clinical MRIs and compared to a third group, 20 non-MS neurological control patients. RESULTS: Compared to healthy controls, MS patients showed impaired GDT and neuropsychological performance, depending on the MS-subtype (relapsing-remitting (RR), n = 22; secondary progressive, n = 10) and disability severity among RR-MS patients. In MS patients, GDT performance correlated with processing speed, intercaudate ratio, and third ventricle ratio (p's < 0.05). Mediation analysis showed that the link between GDT performance and processing speed was fully explained by ventricular size. CONCLUSION: Decision-making under explicit risk was reduced in MS patients, but only those with more pronounced disability. Independent of processing speed, decision-making under explicit risk correlates inversely with central atrophy in MS.
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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 | 0.000 | 0.002 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.000 | 0.000 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.000 | 0.000 |
| Open science | 0.000 | 0.000 |
| Research integrity | 0.000 | 0.000 |
| 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".