Fatigue in multiple sclerosis: association with disease-related, behavioural and psychosocial factors
Bibliographic record
Abstract
We determined biopsychosocial correlates of general, physical, and mental fatigue in MS patients, by evaluating the additional contribution of potentially modifiable factors after accounting for non-modifiable disease-related factors. Fifty-three ambulatory MS patients, along with 28 normal controls were recruited for a cross-sectional study. Subjects completed the Multidimensional Fatigue Inventory (MFI) and Fatigue Severity Scale. Potential correlates evaluated were: disease-related factors (disease duration and type, immunomodulating treatment, muscle strength, pain, forced vital capacity (FVC), respiratory muscle strength, body mass index, disability, fibromyalgia), behavioural factors (physical activity, sleep quality) and psychosocial factors (depression, stress, self-efficacy). Multivariate models were calculated for MFI General, Physical, and Mental Fatigue. Age-adjusted multivariate models with non-modifiable factors included the following predictors (P < or = 0.10) of 1) MFI General and Mental Fatigue: none; and 2) MFI Physical Fatigue: FVC and disability. The following potentially modifiable predictors (P < or = 0.10) made an additional contribution to the models 1) MFI General Fatigue: sleep quality, self-efficacy, pain; 2) MFI Physical Fatigue: self-efficacy, physical activity; and 3) MFI Mental Fatigue: stress, self-efficacy. Fatigue in MS is multidimensional. Correlates of general and physical fatigue are disease-related, behavioural and psychosocial factors. Correlates of mental fatigue are psychosocial factors. Potentially modifiable factors account for a considerable portion of fatigue.
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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 | 0.004 |
| 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 | 0.001 | 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".