Fatigue is a relevant outcome in patients with myasthenia gravis
Bibliographic record
Abstract
INTRODUCTION: Patients with myasthenia gravis often experience fatigue, but its effect on quality of life (QoL) is underestimated, and fatigue is rarely measured in clinical trials. METHODS: Two hundred fifty-seven myasthenic patients completed the Neuro-QoL-Fatigue and measures of disease severity and QoL. We studied the relationship between fatigue and clinical and demographic variables. Finally, we studied the responsiveness of the Neuro-QoL-Fatigue in 95 patients receiving treatments for myasthenia and estimated the minimal important difference (MID). RESULTS: Fatigue correlated with greater disease severity (r = 0.52-0.69, P < 0.0001) and worse QoL (r = 0.65-0.75, P < 0.0001). Patients in remission, with minimal manifestations, and pure ocular symptoms reported minimal fatigue. Regression modeling showed that, in addition to its relationship with disease severity, fatigue was worse in females, patients with generalized disease, and those with anxiety/depression. Fatigue improved after immunomodulation (P < 0.0001), and the MID was 5.3 points. DISCUSSION: Fatigue in myasthenia correlates with disease severity, affects QoL, and can improve after treatment. Muscle Nerve 58: 197-203, 2018.
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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 | 0.000 | 0.000 |
| 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) | 0.002 | 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".