Using ultrasound elastography to assess non-invasive, non-pharmacological interventions for musculoskeletal stiffness: a systematic review and meta-analysis
Bibliographic record
Abstract
Purpose To evaluate the current evidence regarding the use of ultrasound elastography for assessing non-invasive, non-pharmacological interventions for eliciting changes in musculoskeletal stiffness.Methods 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.Results 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 (g = 1.488, 95% CI = 0.320–2.655, p = 0.013). Analyses for other intervention types and patient groups were underpowered.Conclusion 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.
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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 | 0.017 | 0.048 |
| Meta-epidemiology (narrow) | 0.003 | 0.001 |
| Meta-epidemiology (broad) | 0.023 | 0.022 |
| Bibliometrics | 0.011 | 0.009 |
| Science and technology studies | 0.001 | 0.001 |
| Scholarly communication | 0.004 | 0.002 |
| Open science | 0.002 | 0.002 |
| Research integrity | 0.003 | 0.002 |
| 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".