A Cross‐Sectional Study of Quality of Life and Body Image in Myasthenia Gravis Patients: A Novel Approach Using the Individualized Neuromuscular Quality of Life Questionnaire
Bibliographic record
Abstract
INTRODUCTION/AIMS: Many generic and disease-specific quality of life (QoL) measures have been used to understand the perspectives of patients with myasthenia gravis (MG). However, there is limited data regarding the use of the Individualized Neuromuscular Quality of Life Questionnaire (INQoL) and the impact of body image in patients with MG. METHODS: This was a cross-sectional cohort of 258 patients with MG who completed several QoL measures, including the INQoL, 36-Item Short Form Survey (SF-36) and 15-Item Myasthenia Gravis Quality of Life Scale (MG-QoL15). We compared scores of different QoL measures with each other and also compared SF-36 scores to the general population. Linear regression models were built to investigate factors associated with QoL and body image in MG patients. RESULTS: MG patients had lower SF-36 scores compared to the general population. Of the different QoL measures, the INQoL correlated the strongest with the MG-QoL15 (r = 0.80, p < 0.05). Worse QoL (measured by the INQoL) was significantly correlated with increased disease severity (p = 0.0054) and fatigue (p = 0.0019), younger age (p = 0.0471), and retirement (p = 0.0450). Worse INQoL body image scores were significantly associated with increased fatigue (p = 0.0189) and ptosis severity (p = 0.0298). DISCUSSION: The INQoL showed that body image is negatively affected in people with MG, suggesting it poses a burden and may be worth considering when measuring QoL. Further studies are needed to assess other factors associated with reduced body image, besides ptosis and fatigue, in people living with MG.
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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 | 0.002 | 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) | 0.001 | 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".