A Longitudinal Evaluation of Cognitive Fatigue on a Task of Sustained Attention in Early Relapsing-Remitting Multiple Sclerosis
Bibliographic record
Abstract
BACKGROUND: Cognitive fatigue can be objectively measured on tasks of sustained attention and can be defined as decreased performance as a result of sustained cognitive effort. Individuals with multiple sclerosis (MS) early in their disease are vulnerable to cognitive fatigue, although this has yet to be evaluated longitudinally. We aimed to evaluate cognitive fatigue over a 3-year interval in individuals with early-phase relapsing-remitting MS (RRMS). The sensitivity of the Paced Auditory Serial Addition Test (PASAT) at detecting cognitive fatigue was evaluated, as was the impact of scoring method. METHODS: 32 people with MS and 32 controls completed the 3- and 2-second PASAT (PASAT-3″ and -2″) as a measure of sustained attention at baseline and 3-year follow-up. RESULTS: Performance on the PASAT remained stable across time, with improvement noted on the PASAT-2″ likely due to practice and the small sample size. Cognitive fatigue was noted at both times, although sensitivity varied based on scoring method. No evidence of worsening cognitive fatigue was noted over time. The MS group performed worse only when cognitive fatigue was the outcome variable. CONCLUSIONS: Although individuals with MS continue to be vulnerable to cognitive fatigue at follow-up, severity does not seem to increase with time. Cognitive fatigue may be a more sensitive marker of cognitive impairment than overall task performance in those with early-phase RRMS, which has important implications given that clinically only task performance is typically assessed.
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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 | 0.002 | 0.003 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.001 | 0.000 |
| Science and technology studies | 0.001 | 0.000 |
| Scholarly communication | 0.000 | 0.000 |
| Open science | 0.000 | 0.000 |
| Research integrity | 0.001 | 0.001 |
| 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".