Neuromuscular Ultrasound: Clinical Applications and Diagnostic Values
Bibliographic record
Abstract
Advances in high-resolution ultrasound have provided clinicians with unique opportunities to study diseases of the peripheral nervous system. Ultrasound complements the clinical and electrophysiology exam by showing the degree of abnormalities in myopathies, as well as spontaneous muscle activities in motor neuron diseases and other disorders. In experienced hands, ultrasound is more sensitive than MRI in detecting peripheral nerve pathologies. It can also guide needle placement for electromyography exam, therapeutic injections, and muscle biopsy. Ultrasound enhances the ability to detect carpal tunnel syndrome and other focal nerve entrapment, as well as pathological nerve enlargements in genetic and acquired neuropathies. Furthermore, ultrasound can potentially be used as a biomarker for muscular dystrophy and spinal muscular atrophy. The combination of electromyography and ultrasound can increase the diagnostic certainty of amyotrophic lateral sclerosis, aid in the localization of brachial plexus or peripheral nerve trauma and allow for surveillance of nerve tumor progression in neurofibromatosis. Potential limitations of ultrasound include an inability to image deeper structures, with lower sensitivities in detecting neuromuscular diseases in young children and those with mitochondrial myopathies, due to subtle changes or early phase of the disease. As well, its utility in detecting critical illness neuromyopathy remains unclear. This review will focus on the clinical applications of neuromuscular ultrasound. The diagnostic values of ultrasound for screening of myopathies, neuropathies, and motor neuron diseases will be presented.
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How this classification was reachedexpand
Full frame distilled prediction
Teacher imitationNot calibrated prevalence, not ground truth. Human validation pending. Learned from the 10,348 direct Codex labels and 10,348 direct Gemma labels. Candidate is the union of thresholded teacher heads; consensus is their intersection. These outputs are machine_predicted_unvalidated and are not human labels or direct frontier model labels.
Codex and Gemma teacher scores by category
| Category | Codex | Gemma |
|---|---|---|
| Metaresearch | 0.010 | 0.018 |
| Meta-epidemiology (narrow) | 0.001 | 0.000 |
| Meta-epidemiology (broad) | 0.002 | 0.001 |
| Bibliometrics | 0.001 | 0.002 |
| Science and technology studies | 0.003 | 0.017 |
| Scholarly communication | 0.001 | 0.001 |
| Open science | 0.001 | 0.000 |
| Research integrity | 0.001 | 0.002 |
| Insufficient payload (model declined to judge) | 0.000 | 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; both teacher heads agree on what is shown here.
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".