Not so Shocking: Electromyography in Pediatrics Remains Feasible and Diagnostically Useful
Bibliographic record
Abstract
BACKGROUND: Electrodiagnostic testing, including nerve conduction studies (NCS) and electromyography (EMG), assists with localizing lesions within the peripheral nervous system. NCS/EMG in children can be technically challenging and its relevance has been questioned in the era of affordable genetic testing. NCS/EMG provides information that may not be available in the examination of a young or developmentally delayed child. Our goal was to review the volume and referral sources of NCS/EMG studies and evaluate its feasibility and diagnostic yield at a pediatric tertiary care hospital. METHODS: Retrospective chart review of NCS/EMG studies done in pediatric patients at one center from 2014 to 2019. RESULTS: A total of 725 studies were performed, with a median age of 13.2 years (range 0-18 years). The annual number of studies remained constant throughout the study period. Neurologists and surgeons were the most common referral sources, but an increased number of referrals from geneticists was observed. Most (94.5%) NCS/EMG were done on awake patients, with only 5.5% of studies being terminated early due to tolerability of the patient. Of all studies, 326/725 (44%) demonstrated a neuromuscular abnormality, of which 63.5% (207/326) were acquired conditions. Mononeuropathies and polyneuropathies were the most common electrophysiologic diagnoses. DISCUSSION: Our study indicates that NCS/EMG remains a useful diagnostic tool, both for the diagnosis of acquired neuromuscular conditions but also as an adjunct for interpreting genetic results, as indicated by the recent increase in referrals from geneticists. Overall NCS/EMG is well tolerated and able to be performed without sedation in children of all ages.
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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 | 0.019 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.001 | 0.001 |
| Science and technology studies | 0.001 | 0.001 |
| Scholarly communication | 0.002 | 0.003 |
| Open science | 0.001 | 0.001 |
| Research integrity | 0.001 | 0.001 |
| Insufficient payload (model declined to judge) | 0.003 | 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".