Using ultrasound elastography to assess non-invasive, non-pharmacological interventions for musculoskeletal stiffness: a systematic review and meta-analysis
Bibliographic record
Abstract
To evaluate the current evidence regarding the use of ultrasound elastography for assessing non-invasive, non-pharmacological interventions for eliciting changes in musculoskeletal stiffness. A systematic search of MEDLINE, CINAHL, EMBASE, and Web of Science databases was performed in accordance with Preferred Reporting Items for Systematic Review and Meta-Analysis (PRISMA) guidelines. Information on measurement and intervention procedures was extracted. Bias was assessed using Cochrane Risk of Bias or Risk of Bias In Non-randomised Studies of Interventions (ROBINS-I) tools for studies with true or quasi-experimental designs, respectively. Analyses were conducted for adequately powered subgroups based on intervention type, measurement site, and population assessed. Twenty-one studies were included in the review. Overall risk of bias was low for true experimental studies and moderate for quasi-experimental studies. Subgroup analyses indicated a large overall effect for interventions involving manual physiotherapy and taping/splinting for reducing masseter muscle stiffness in patients with masticatory muscle disorders (<i>g</i> = 1.488, 95% CI = 0.320–2.655, <i>p</i> = 0.013). Analyses for other intervention types and patient groups were underpowered. Ultrasound elastography demonstrates clinical applicability for assessing non-invasive, non-pharmacological interventions for musculoskeletal stiffness. However, the comparative efficacy of these interventions for modulating tissue stiffness remains inconclusive. Elastography demonstrates clinical applicability for assessing non-invasive, non-pharmacological interventions for musculoskeletal stiffness Interventions involving manual physiotherapy and taping/bracing showed a large overall effect for reducing masseter muscle stiffness The comparative efficacy of other interventions remains inconclusive
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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 | 0.001 | 0.007 |
| Meta-epidemiology (narrow) | 0.001 | 0.001 |
| Meta-epidemiology (broad) | 0.013 | 0.015 |
| Bibliometrics | 0.002 | 0.005 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.000 | 0.000 |
| Open science | 0.001 | 0.000 |
| Research integrity | 0.000 | 0.001 |
| Insufficient payload (model declined to judge) | 0.034 | 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".