Exposure to Low Amounts of Ultrasound Energy Does Not Improve Soft Tissue Shoulder Pathology: A Systematic Review
Bibliographic record
Abstract
BACKGROUND: Although therapeutic ultrasound is commonly used to treat shoulder injuries, research to date on the ability of ultrasound to improve outcomes for shoulder pathologies is conflicting. OBJECTIVE: This study aimed to systematically and critically review available literature to ascertain whether beneficial effects of ultrasound were associated with certain shoulder pathologies or particular ultrasound treatment protocols. METHODS: Five electronic databases were searched, and the included studies, identified through pair consensus, were randomized controlled trials (RCTs) that utilized ultrasound for soft tissue shoulder injury or pain. STUDY SELECTION AND DATA EXTRACTION: Eight studies included in this review (n=586 patients, median PEDro score=8.0/10) evaluated various parameters, including the duration of patients' symptoms (0-12 months), duty cycle (20% and 100%), intensity (0.1-2.0 W/cm2), treatment time per session (4.5-15.8 minutes), number of treatments (6-39), and total energy applied per treatment (181-8,152 J). DATA SYNTHESIS: Inconsistent outcome measures among studies precluded meta-analysis; however, 3 RCTs showed statistically significant benefits of ultrasound, 2 of which examined calcific tendinitis. Studies that showed beneficial effects of ultrasound typically had 4 times longer total exposure times and applied much greater ultrasound energy per session (average of 4,228 J) compared with studies that showed no benefit of ultrasound (average of 2,019 J). No studies that delivered < or = 720 J per session showed improvement in treatment groups. LIMITATIONS: Current research involving ultrasound treatment protocols that delivered low levels of ultrasound energy do not adequately address whether ultrasound can improve outcomes for shoulder disorders. CONCLUSION: Determining whether therapeutic ultrasound can affect soft tissue shoulder pathologies will require further research and systematic reviews that involve appropriate ultrasound treatment protocols.
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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 | 0.012 | 0.056 |
| Meta-epidemiology (narrow) | 0.001 | 0.001 |
| Meta-epidemiology (broad) | 0.011 | 0.010 |
| Bibliometrics | 0.008 | 0.009 |
| Science and technology studies | 0.001 | 0.001 |
| Scholarly communication | 0.003 | 0.002 |
| Open science | 0.002 | 0.001 |
| Research integrity | 0.002 | 0.001 |
| 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".