The influence of fatigue on the daily functioning of multiple sclerosis patients
Bibliographic record
Abstract
Abstract Introduction Fatigue is a complex and often debilitating symptom of multiple sclerosis (MS), affecting a large number of individuals with the condition. Research has shown that fatigue and impaired mobility are the two main causes of work loss in people with MS, with fatigue being one of the leading causes of unemployment. Aim The research aimed to study the impact of fatigue on individuals with MS, including its effects on physical functioning, daily life activities, work, family, and social life. Material and method The data was collected using the Fatigue Assessment Scale, a tool specifically designed for individuals with multiple sclerosis to assess the impact of fatigue on their work, home, and school life. The Fatigue Severity Scale measures fatigue levels, which distinguishes fatigue from clinical depression due to overlapping symptoms. Approximately 700 participants from all over the world participated in the study, with the majority coming from the USA, Canada, and Germany. Results The results indicate that fatigue presents a significant challenge for individuals with MS, impacting activities of daily life including leisure, work, and treatment (kinesitherapy). The results also show a connection between gender and fatigue, although the dependence or independence between the two was not determined. Our findings suggest that fatigue is one of the three symptoms that causes significant difficulties for people with MS, affecting all areas of their functionality. Conclusions In conclusion, this scientific paper highlights the importance of addressing fatigue in individuals with MS, as it can have a significant impact on their quality of life. Effective management strategies are essential to ensure the health, well-being, and recovery of affected SM patients. Further research is needed to understand the various causes of fatigue in MS and to develop effective interventions to address it.
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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 | 0.001 | 0.000 |
| Open science | 0.000 | 0.000 |
| Research integrity | 0.000 | 0.000 |
| 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".