Longitudinal relationships between free‐living activities, fatigue, and symptom severity in myasthenia gravis using cohort and individualized models
Bibliographic record
Abstract
INTRODUCTION/AIMS: Fluctuating symptoms and fatigue are common issues in myasthenia gravis (MG), but it is unclear if these symptoms are related to physical activity or sleep patterns. This study sought to determine the day-to-day relationship between patient-reported symptoms and physical activity and sleep over 12 weeks. METHODS: Sixteen participants with generalized MG wore a wrist-mounted accelerometer continuously for the study duration and reported their symptoms and fatigue each evening. Cumulative link mixed models were used to analyze whether clinical and demographic characteristics, physical activity, and sleep were related to symptom severity and fatigue over the study period. Three types of models were constructed: a cohort model, a model in which data was scaled to each participant, and individual models. RESULTS: The cohort model indicated that higher disease severity, female sex, more comorbidities, less physical activity, more inactive time, and lower quantity of sleep were significantly associated with increased symptom severity and fatigue (p < .05). However, in the within-participant scaled model, there were almost no significant associations with physical activity or sleep. In the individual models, some participants showed similar results to the cohort model, but others showed no associations or the opposite response in some variables. DISCUSSION: While physical activity and sleep were associated with self-reported symptoms and fatigue within this population, this was not necessarily applicable to individuals. This demonstrates the importance of an individualized analysis for determining how physical activity and sleep may impact outcomes in MG, with implications for clinical and self-management.
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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 | 0.020 | 0.018 |
| Meta-epidemiology (narrow) | 0.001 | 0.001 |
| Meta-epidemiology (broad) | 0.001 | 0.004 |
| Bibliometrics | 0.001 | 0.001 |
| Science and technology studies | 0.001 | 0.001 |
| Scholarly communication | 0.002 | 0.001 |
| Open science | 0.002 | 0.002 |
| Research integrity | 0.001 | 0.002 |
| Insufficient payload (model declined to judge) | 0.002 | 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".