Indices of Cognitive Dysfunction in Relapsing-Remitting Multiple Sclerosis: Intra-individual Variability, Processing Speed, and Attention Network Efficiency
Bibliographic record
Abstract
Impairments in attention and information processing speed are common in multiple sclerosis (MS) and may contribute to impairments of other cognitive abilities. This study examined attentional efficiency, information processing speed and intra-individual variability in response speed using the Attention Network Test-Interactions (ANT-I) in mildy-affected patients with MS. Thirty-one patients with relapsing-remitting MS and 30 age, sex, and education-matched controls completed the ANT-I, as well as the Paced Auditory Serial Attention Test (PASAT), as a standard clinical measure of information processing efficiency. As expected, patients with MS were slower in reaction time performance on the ANT-I and had poorer performance on the PASAT compared to controls. Patients with MS also demonstrated poorer efficiency in their executive control of attention on the ANT-I, suggesting difficulties with top-down allocation of attention. In addition, the MS group demonstrated greater intra-individual variability in the responses to the ANT-I even when their slower overall response time and other factors such as practice were accounted for. Intra-individual variability was found to best predict group membership compared to PASAT scores and other ANT-I scores. These results suggest that intra-individual variability may provide sensitive, unique and important information regarding cognitive functioning in early MS.
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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 | 0.003 |
| Meta-epidemiology (narrow) | 0.001 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.001 | 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) | 0.001 | 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".