Ultrasound imaging for sarcopenia, spasticity and painful muscle syndromes
Bibliographic record
Abstract
PURPOSE OF REVIEW: On the basis of its various advantages and the relevant awareness of physicians, ultrasound imaging has overwhelmingly taken its place in the scientific arena. This is true both from the side of daily clinical applications and also from the side of research. Yet, ultrasound provides real-time (diagnostic) imaging and (interventional) guidance for a wide spectrum of muscle disorders. In this regard, this review aims to discuss the potential/actual utility of ultrasound imaging in particular muscle disorders, that is, sarcopenia, spasticity and fibromyalgia/myofascial pain syndrome. RECENT FINDINGS: Due to the aging population worldwide and the importance of functionality in the older population, mounting interest has been given to the diagnosis and management of sarcopenia in the recent literature. Likewise, several articles started to report that ultrasound imaging can be used conveniently and effectively in the early diagnosis and quantification of sarcopenia.For spasticity, aside from ultrasound-guided botulinum toxin injections, intriguing attention has been paid to sonographic evaluation of muscle architecture, echogenicity and elasticity in the follow-up of these chronic conditions.As regards painful muscle syndromes, quantitative ultrasound techniques have been shown to detect statistically significant differences between healthy controls and patients with myofascial pain syndrome. SUMMARY: Ultrasound imaging seems to be a promising tool that indisputably deserves further research in the management of a wide range of muscle disorders. VIDEO ABSTRACT: http://links.lww.com/COSPC/A17.
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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 | 0.002 |
| Meta-epidemiology (narrow) | 0.001 | 0.000 |
| Meta-epidemiology (broad) | 0.001 | 0.001 |
| Bibliometrics | 0.003 | 0.002 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.001 | 0.001 |
| Open science | 0.001 | 0.000 |
| Research integrity | 0.001 | 0.001 |
| Insufficient payload (model declined to judge) | 0.013 | 0.005 |
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".