P.098 Does single fiber EMG (SFEMG) pair number influence the outcome of patients initially referred for possible myasthenia gravis (MG)?
Bibliographic record
Abstract
Background: Analysis of 20 pairs is the traditional standard when using SFEMG to diagnose MG. Some studies show that fewer pairs are needed if results are normal. We examined what impact this might have on long-term outcomes. Methods: Hospital charts of 239 consecutive patients who underwent SFEMG between January 2011, and July 18th, 2024, were reviewed. Results: 201 patients were identified; 128 had normal SFEMGs. Of the patients with normal SFEMGs, 58 (45.31%) had 12 pairs observed and 69 (53.91%) had 20 or more pairs observed. In the 12 pair group, 1(1.72%) patient had delayed MG diagnosis, and 2 (3.45%) patients were referred for repeat SFEMGs; in the 20 or more group, 2 (2.90%) patients belong in each aforementioned category. No patients from either group were hospitalized for MG after SFMEG. Conclusions: Preliminary results demonstrate no difference in frequency of poor outcomes between patients who had 20 or more pairs observed and those who had 12 pairs observed, supporting the safety of shortening the test in appropriate situations.
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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 | 0.005 |
| 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) | 0.005 | 0.001 |
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".