Assessing muscle architecture with ultrasound: implications for spasticity
Bibliographic record
Abstract
Botulinum Neurotoxin Type A (BoNT-A) injections using Ultrasound (US) guidance have led to research evaluating changes in muscle architecture. Controversy remains as to what constitutes increased Echo-Intensity (EI) in spastic muscles and whether this may affect outcomes. We aim to provide a narrative review of US muscle architecture changes following Central Nervous System (CNS) lesions and explore their relationship to spasticity. Medline, CINAHL, and Embase databases were searched with keywords: ultrasonography, hypertonia, spasticity, fibrosis, and Heckmatt. Three physicians reviewed the results of the search to select relevant papers. Reviews identified in the search were used as a resource to identify additional studies. A total of 68 papers were included. Four themes were identified, including histopathological changes in spastic muscle, effects of BoNT-A on the muscle structure, available US modalities to assess the muscle, and utility of US assessment in clinical spasticity. Histopathological studies revealed atrophic and fibro-fatty changes after CNS lesions. Several papers described BoNT-A injections contributing to those modifications. These changes translated to increased EI. The exact significance of increased muscle EI remains unclear. The Modified Heckmatt Scale (MHS) is a validated tool for grading muscle EI in spasticity. The use of the US may be an important tool to assess muscle architecture changes in spasticity and improve spasticity management. Treatment algorithms may be developed based on the degree of EI. Further research is needed to determine the incidence and impact of these EI changes in spastic muscles.
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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 | 0.000 | 0.000 |
| 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) | 0.000 | 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; a candidate call from one teacher head, 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".