Predictive Models of Cognitive Fatigue in Multiple Sclerosis
Bibliographic record
Abstract
OBJECTIVE: Cognitive fatigue (CF) can be defined as decreased performance with sustained cognitive effort. The present study examined the interrelatedness of disease severity, fatigue, depression, and sleep quality in order to evaluate their predictive roles of CF in MS. Four theoretical models examining these variables were assessed. METHODS: Fifty-eight individuals with a diagnosis of MS were recruited. CF was measured by examining last third versus first third performance on the Paced Auditory Serial Addition Test (PASAT). The PASAT and self-report measures of fatigue, depression, and sleep quality were administered. Path analysis was used to evaluate each of the models. RESULTS: CF was correlated only with depression (r = .362, p = .006) and sleep quality (r = .433, p = .001). Sleep quality was the greatest significant independent predictor of CF (β = .433, t(1,55) = 3.53, p < .001), accounting for 17.3% of the total variance. The best fitting model showed sleep quality as the largest contributor to CF; however, depression played a smaller predictive role. Furthermore, depression emerged as the strongest predictor of sleep quality and fatigue. Disease severity weakly predicted depression. CONCLUSIONS: Sleep quality is the most significant predictor of CF in MS. As such, sleep quality may be a treatable cause of CF. Sleep quality itself, however, accounted for only 17.3% of the variance in CF suggesting that other variables which were not formally assessed in this sample (e.g., anxiety, etc.) may also play a predictive role. Follow-up studies should evaluate how results may differ with a larger sample size.
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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 | 0.005 | 0.020 |
| Meta-epidemiology (narrow) | 0.001 | 0.001 |
| Meta-epidemiology (broad) | 0.001 | 0.002 |
| Bibliometrics | 0.002 | 0.001 |
| Science and technology studies | 0.001 | 0.001 |
| Scholarly communication | 0.001 | 0.001 |
| Open science | 0.001 | 0.001 |
| Research integrity | 0.001 | 0.001 |
| Insufficient payload (model declined to judge) | 0.004 | 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".