Ultrasound Guidance to Augment Needle Electromyography Precision in the Complex Nerve Injury Setting
Bibliographic record
Abstract
The rise in popularity of nerve transfer surgery in individuals with peripheral nerve and spinal cord injuries has elevated the importance of the preoperative electrodiagnostic examination. Needle electromyography (EMG) provides peripheral nerve surgeons with precise information about donor and recipient muscle health, aiding in decisions regarding surgical options, donor muscle viability, and timing of intervention. However, traditional anatomical landmarks for typical donor and recipient nerve-muscle combinations in nerve transfer surgery are either not well described in the literature or become less dependable in the presence of contracture, spasticity, or muscle atrophy and fibrosis. Ultrasound (US) can be a valuable tool to augment the needle EMG examination. Herein, we describe US approaches to improve the precision of the needle EMG examination for 10 muscles in the upper extremity and two muscles in the lower extremity that are routinely involved as either donors or recipients in nerve transfer surgery. The purpose is to provide a reference guide for the electrodiagnostic medicine specialist in the complex nerve injury setting. This includes information on surrounding anatomical structures for localization and those that should be avoided. Relevant US principles for EMG are discussed including: (1) the advantages and disadvantages of short-axis and long-axis views of the target muscle, emphasizing the predominant use of short-axis for adequate visualization of all surrounding structures and enhancing patient safety, (2) in-plane versus out-of-plane approaches, and (3) enhancing confidence in the precision of the needle EMG via the dynamic capability of US.
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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 | 0.006 |
| Meta-epidemiology (narrow) | 0.001 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.001 | 0.001 |
| Science and technology studies | 0.000 | 0.001 |
| Scholarly communication | 0.001 | 0.002 |
| 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".