Precision Health Approach To Daily Activities And Symptom Severity In Generalized Myasthenia Gravis
Bibliographic record
Abstract
Myasthenia gravis (MG) is an autoimmune neuromuscular disease that results in fatigable skeletal muscle weakness. Symptom severity typically fluctuates throughout and between days, impacting quality of life. Previous studies have shown conflicting results as to whether physical activity (PA) and sleep are cross-sectionally related to symptom severity (SYM), but the longitudinal relationship within individuals has not been investigated. PURPOSE: To determine whether day-to-day differences in PA and sleep, as observed via a wearable device, are related to fluctuations in self-reported SYM. METHODS: PA and sleep metrics (moderate-vigorous physical activity, light physical activity, steps, inactivity time, sleep duration, sleep efficiency, number of awakenings) were obtained via a wrist-worn accelerometer for 16 participants over 12 weeks. Participants completed nightly surveys reporting their SYM for that day. Continuous variables were scaled within each participant. One group-based and 16 individual cumulative link mixed models were employed to determine the longitudinal associations between PA/sleep metrics and SYM. In the group model, patient characteristics (disease severity, sex, number of comorbidities) were also included. RESULTS: In the group model, female sex, disease severity, and comorbidities were significantly (p < 0.05) positively correlated to SYM (Fig 1). In the individual models, various PA and sleep factors were related to SYM (Fig 2). CONCLUSIONS: PA and sleep may be related to daily fluctuations in MG SYM and precision health/individual models can help determine relevant outcomes.This work was partially funded by the NSERC CREATE Wearable Technology and Collaboration (We-TRAC) Training Program (Project No. CREATE/511166-229 2018), and the Canadian Neuromuscular Disease Registry (CNDR)
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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 | 0.006 | 0.011 |
| Meta-epidemiology (narrow) | 0.001 | 0.000 |
| Meta-epidemiology (broad) | 0.001 | 0.002 |
| Bibliometrics | 0.002 | 0.002 |
| Science and technology studies | 0.000 | 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) | 0.002 | 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".