Sonoelastography of the Shoulder: A Narrative Review
Bibliographic record
Abstract
qualitative and quantitative biomechanical properties of various tissues. Two types of sonoelastography (SE) are commonly explored: strain and shear wave. Sonoelastography can be used in multiple medical subspecialties to assess pathological tissular changes by obtaining mechanical properties, shear wave speed, and strain ratio data. Although there are various radiological imaging methods, such as MRI or CT scan, to assess musculoskeletal structures (muscles, tendons, joint capsules), SE is more accessible since this approach is of low cost and does not involve radiation. As of 2018, SE has garnered promising data in multiple studies. Preliminary clinico-radiological correlations have been established to bridge tissue biomechanical findings with their respective clinical pathologies. Specifically, concerning the shoulder complex, recent findings have described mechanical tissue changes in shoulder capsulitis. The long head of the biceps and supraspinatus SE were among the recently studied structures with conditions regarding impingement, tendinosis, and tears. Since ultrasonography has established itself as an important tool in shoulder evaluation, it completes the history and physical examination skills of the clinicians. This study will provide an update on the most recent findings on SE of shoulder structures.
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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 | 0.001 | 0.003 |
| Meta-epidemiology (narrow) | 0.001 | 0.000 |
| Meta-epidemiology (broad) | 0.002 | 0.001 |
| Bibliometrics | 0.007 | 0.007 |
| Science and technology studies | 0.000 | 0.001 |
| Scholarly communication | 0.001 | 0.002 |
| Open science | 0.001 | 0.001 |
| Research integrity | 0.001 | 0.001 |
| 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".