Is musculoskeletal pain associated with increased muscle stiffness? Evidence map and critical appraisal of muscle measurements using shear wave elastography
Bibliographic record
Abstract
INTRODUCTION AND AIMS: Approximately 21% of the world's population suffers from musculoskeletal conditions, often associated with sensations of stiff muscles. Targeted therapy requires knowing whether typically involved muscles are objectively stiffer compared to asymptomatic individuals. Muscle stiffness is quantified using ultrasound shear wave elastography (SWE). Publications on SWE-based comparisons of muscle stiffness between individuals with and without musculoskeletal pain are increasing rapidly. This work reviewed and mapped the existing evidence regarding objectively measured muscle stiffness in musculoskeletal pain conditions and surveyed current methods of applying SWE to measure muscle stiffness. METHODS: A systematic search was conducted in PubMed and CINAHL using the keywords "muscle stiffness", "shear wave elastography", "pain", "asymptomatic controls" and synonyms. The search was supplemented by a hand search using Google Scholar. Included articles were critically appraised with the AXIS tool, supplemented by items related to SWE methods. Results were visually mapped and narratively described. RESULTS: Thirty of 137 identified articles were included. High-quality evidence was missing. The results comprise studies reporting lower stiffness in symptomatic participants, no differences between groups and higher stiffness in symptomatic individuals. Results differed between pain conditions and muscles, and also between studies that examined the same muscle(s) and pathology. The methods of the application of SWE were inconsistent and the reporting was often incomplete. CONCLUSIONS: Existing evidence regarding the objective stiffness of muscles in musculoskeletal pain conditions is conflicting. Methodological differences may explain most of the inconsistencies between findings. Methodological standards for SWE measurements of muscles are urgently required.
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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 | 0.066 | 0.293 |
| Meta-epidemiology (narrow) | 0.003 | 0.003 |
| Meta-epidemiology (broad) | 0.012 | 0.010 |
| Bibliometrics | 0.036 | 0.019 |
| Science and technology studies | 0.002 | 0.005 |
| Scholarly communication | 0.009 | 0.007 |
| Open science | 0.005 | 0.004 |
| Research integrity | 0.005 | 0.003 |
| Insufficient payload (model declined to judge) | 0.005 | 0.001 |
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".