Methodological Implementation of Ultrasound-Based Shear Wave Elastography in Individuals With Knee Osteoarthritis: A Scoping Review
Bibliographic record
Abstract
Ultrasound shear wave elastography (SWE) enables the reliable assessment of tissue stiffness in vivo. Recently, SWE has emerged as a tool to study musculoskeletal tissue mechanics in healthy and pathological populations, such as individuals with knee osteoarthritis (KOA). No previous articles have explicitly reviewed the implementation of SWE methodology in KOA populations. Therefore, a scoping review of published literature was performed to investigate study equipment details, measurement protocols and participant/transducer positioning as they relate to SWE methodology. The databases PubMed, CINAHL, Scopus and Embase were searched for published literature, yielding n = 29 articles that met inclusion criteria. Results were summarized by tissue type under investigation (n = 7, muscle, tendon, adipose, multiple tissues, cartilage, nerve and joint capsule). Examined in over half of all included studies (55%), muscle was the predominant tissue under investigation, while relatively few studies focused on direct KOA-related structures, such as cartilage. Transducer type and orientation were the only relatively homogenous SWE methods, with 93% of all included studies using linear transducers and 91% of reporting studies using parallel orientation. Notable heterogeneity in SWE methods was present across device modes, settings, stiffness measures and body, knee and transducer positioning. These findings indicate that methodological variability remains a significant challenge for SWE research in KOA, potentially limiting the reproducibility and generalizability of the stiffness outcomes that drive our current understanding of tissue mechanics in this population. Collectively, these results highlight the need for tissue-specific SWE methodology standardization to enhance the reliability, comparability and clinical interpretability of SWE in KOA research.
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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 | 0.089 | 0.307 |
| Meta-epidemiology (narrow) | 0.002 | 0.002 |
| Meta-epidemiology (broad) | 0.007 | 0.009 |
| Bibliometrics | 0.023 | 0.019 |
| Science and technology studies | 0.002 | 0.003 |
| Scholarly communication | 0.007 | 0.005 |
| Open science | 0.004 | 0.005 |
| Research integrity | 0.004 | 0.002 |
| Insufficient payload (model declined to judge) | 0.004 | 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".