Recording Fewer Than 20 Potential Pairs With SFEMG May Suffice for the Diagnosis of Myasthenia Gravis
Bibliographic record
Abstract
PURPOSE: Our aim in the current study was to determine the minimum number of SFEMG potential pairs required to confirm neuromuscular junction impairment and relate this number to disease severity. METHODS: Ninety-four patients with myasthenia gravis (MG) attending the neuromuscular clinic from February 2013 to November 2015 were included. The SFEMG sensitivity was determined for each number of recorded pairs up to 20. In addition, we compared clinical and electrophysiologic characteristics between patients with abnormality within the first 3, 4 to 7, and ≥8 recorded pairs to determine whether this number is associated with disease severity. RESULTS: Ninety-eight percent of patients had abnormal SFEMG, within 17 pairs in ocular MG, and within 15 pairs in generalized MG. All patients with generalized MG had at least one abnormal pair in the first five recorded pairs. Patients with three abnormal pairs apparent earlier during the test had more frequent bulbar, respiratory, and limb muscle weakness, and had higher mean jitter values and decrement values. CONCLUSIONS: In most cases, an abnormal SFEMG examination can be demonstrated in the first 15 recorded potential pairs in patients with generalized MG, and in the first 17 pairs in patients with ocular MG, thus shortening the test time and decreasing patient discomfort while preserving test sensitivity.
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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 | 0.005 |
| Meta-epidemiology (narrow) | 0.001 | 0.000 |
| Meta-epidemiology (broad) | 0.001 | 0.000 |
| Bibliometrics | 0.001 | 0.000 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.001 | 0.001 |
| Open science | 0.000 | 0.000 |
| Research integrity | 0.001 | 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".