A systematic review on sonoelastography for rotator-cuff post-repair assessment
Bibliographic record
Abstract
Surgical repair of rotator cuff tears is performed routinely; however, the risks of re-tears and the associated consequences are significant. Sonoelastography, an imaging modality that evaluates the mechanical properties of tissues, can examine the dynamic transitions in rotator cuff stiffness following retear and investigate the relationship between these changes and the occurrences of retears. This systematic review aimed to summarize the role of perioperative sonoelastography in repaired rotator cuffs. A comprehensive search of the PubMed, Embase, and Cochrane databases was conducted, covering studies published until June 19, 2023. The Newcastle-Ottawa scale was used for quality assessment. The key information extracted from each study included the injury/surgery type, follow-up duration, sonoelastography mode, and main sonoelastographic findings. Eleven eligible studies comprising 355 patients were included. All studies focused on supraspinatus muscles and tendons with previous arthroscopic repairs. During the postoperative 1st - 6th months, muscle stiffness increased in the supraspinatus and decreased in the ipsilateral deltoid. Failure to recover supraspinatus muscle elasticity might be indicative of potential tendon re-tear; however, it is imperative to first establish correlations with other imaging modalities. Conflicting findings have been observed regarding stiffening or softening of the supraspinatus tendon after surgical repair. The preoperative stiffness of the supraspinatus tendon did not correlate with postoperative tendon integrity or function.
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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 | 0.007 | 0.037 |
| Meta-epidemiology (narrow) | 0.002 | 0.001 |
| Meta-epidemiology (broad) | 0.007 | 0.007 |
| Bibliometrics | 0.012 | 0.011 |
| Science and technology studies | 0.001 | 0.001 |
| Scholarly communication | 0.002 | 0.002 |
| Open science | 0.002 | 0.001 |
| Research integrity | 0.002 | 0.001 |
| Insufficient payload (model declined to judge) | 0.006 | 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".