Multiple Sclerosis Fatigue is Associated with Reduced Psychomotor Vigilance
Bibliographic record
Abstract
BACKGROUND: Fatigue is common and disabling in multiple sclerosis, yet its physiologic substrates remain poorly defined. The aim of this study was to determine the relationship between fatigue and an objective measure of alertness in MS patients. METHODS: This study enrolled 49 consecutive MS clinic patients at two academic hospitals in Toronto. Alertness was assessed with the psychomotor vigilance test (PVT), a ten-minute reaction-time test that measures attention and is sensitive to sleep loss. Patients with visual impairment or arm weakness were excluded. Validated tools were used to assess fatigue, disability, mood, and pain. RESULTS: The average age was 43; 65% were women. Median EDSS was 2.0 (range 0-7.5). Fifty-five percent reported a high impact of fatigue on their lives. Psychomotor vigilance test performance was worse than in an age- and sex-matched population, with a mean reaction time of 315 msecs and 3.98 lapses >500 msec (p<0.001). In a multiple regression analysis, fatigue was the most significantly correlated factor with mean PVT reaction time (p<0.05), and disability was also significantly correlated (p<0.01). Mood and pain did not correlate with the PVT. Eighteen (37%) reported often experiencing restlessness in their legs at night. CONCLUSION: Subjective fatigue and disability were associated with poor performance on alertness testing in MS patients. This research highlights a potential role for psychomotor vigilance testing in providing a standardized assessment tool for an important aspect of MS-related fatigue.
Fetched live from OpenAlex and de-inverted. Abstracts are not stored in this database: the inverted indexes are 8.6 GB of the frame’s 9.3 GB of text, and the host has 13 GB free.
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 | 0.000 | 0.002 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.001 | 0.001 |
| 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) | 0.003 | 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".