Electrophysiological testing is correlated with myasthenia gravis severity
Bibliographic record
Abstract
INTRODUCTION: Electrophysiological studies play an important role in the diagnosis of myasthenia gravis (MG). The objectives of this study was to explore the correlation of jitter and decrement with various clinical symptoms and signs and disease severity. METHODS: We performed a retrospective chart review of 75 MG patients who attended the neuromuscular clinic from April 2013 to May 2014. We compared clinical characteristics between patients with high jitter (>100 µs) and decrement (>10%), and patients with lower values to explore the correlations and optimal thresholds of jitter and decrement for different clinical features. RESULTS: High jitter and decrement values were associated with more severe disease, manifested by more frequent symptomatic bulbar and limb muscle weakness, more frequent ocular and limb muscle weakness on examination, higher quantitative MG score, and generalized disease. CONCLUSIONS: The yield of the electrophysiological assessment in MG extends beyond disease diagnosis and correlates with disease severity and the presence of generalized disease. Muscle Nerve 56: 445-448, 2017.
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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 | 0.000 | 0.003 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.001 | 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".