Sonoelastography to Assess Muscular Stiffness Among Older Adults and its Use for the Diagnosis of Sarcopenia: A Systematic Review
Bibliographic record
Abstract
Changes in muscle stiffness have been reported with sarcopenia. Sonoelastography is an accessible and non-radiating imaging technique allowing quantification of elastic properties of tissue. We performed a systematic review of the literature to investigate whether sonoelastography can be a reliable method to assess sarcopenia in older patients. We searched Medline, Google Scholar, Scopus, SpringerLink and Science direct from January 1, 1990 to April 1, 2020. Three independent review authors assessed trial eligibility, extracted the data, and assessed risk of bias. We intended to learn which types of elastography have been tested, if such measures are repeatable, and if they have been compared to the currently accepted diagnostic method. Ten studies met the inclusion criteria. Most followed a cross-sectional design with young and older adult subgroups. The gastrocnemius, rectus femoris, and vastus intermedius appeared most frequently. Nine of the included studies used shear wave elastography and one-strain elastography. The passive elastic constant was significantly greater in sarcopenic versus healthy subjects after passive stretching (124.98 ± 60.82 vs. 46.35 ± 15.85, P = 0.004). However, even in non-sarcopenic patients, the age of the patient was responsible for about 45.5 % of the variance in SWV. Among ten included articles, four reported higher stiffness in the muscles of older adults, two reported lower stiffness, and four found no significant difference. Due to the substantial heterogenicity of actual data, we could not make any conclusions about the potential usefulness of elastography to assess sarcopenia. Further studies are needed, including a larger sample of older patients and using a standardized and reproducible protocol.
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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 | 0.008 | 0.034 |
| Meta-epidemiology (narrow) | 0.001 | 0.001 |
| Meta-epidemiology (broad) | 0.009 | 0.007 |
| Bibliometrics | 0.013 | 0.012 |
| Science and technology studies | 0.001 | 0.001 |
| Scholarly communication | 0.002 | 0.002 |
| Open science | 0.002 | 0.001 |
| Research integrity | 0.002 | 0.001 |
| Insufficient payload (model declined to judge) | 0.004 | 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 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".