Effects of therapeutic ultrasound on pain, physical functions and safety outcomes in patients with knee osteoarthritis: a systematic review and meta-analysis
Bibliographic record
Abstract
Objective: To explore the effects of therapeutic ultrasound with sham or no intervention on pain, physical function and safety outcomes in patients with knee osteoarthritis. Data sources: This systematic review was searched on CENTRAL, EMBASE, MEDLINE, CINAHL, Physiotherapy Evidence Database, Open Gray on 4 September 2015. Trials included randomized controlled trials that compared therapeutic ultrasound with a sham or no intervention in patients with osteoarthritis of the knee. Review methods: Eligible trials and extracted data were identified by two independent investigators. Standardized mean differences (SMDs) and 95% confidence interval (CI) were calculated for pain and physical function outcomes. Heterogeneity was assessed by the I 2 test and inverse-variance random-effects analysis was applied to all trials. Results: Ten randomized controlled trials (645 patients) met the inclusion criteria. Therapeutic ultrasound showed a positive effect on pain (SMD = −0.93, 95%, CI = −1.22 to −0.64, p < 0.01, p for heterogeneity = 0.12, I 2 = 42%). For physical function, therapeutic ultrasound was advantageous for reducingWestern Ontario and McMaster Universities physical function score (SMD = −0.37, 95% CI = −0.73 to −0.01, p = 0.04, p for heterogeneity = 0.94, I 2 = 0%). In terms of safety, no occurrence of adverse events caused by therapeutic ultrasound was reported in any trial. Conclusion: The authors suggested that therapeutic ultrasound is beneficial for reducing knee pain and improving physical functions in patients with knee osteoarthritis and could be a safe treatment.
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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 | 0.013 | 0.033 |
| Meta-epidemiology (narrow) | 0.002 | 0.001 |
| Meta-epidemiology (broad) | 0.019 | 0.026 |
| Bibliometrics | 0.006 | 0.006 |
| Science and technology studies | 0.001 | 0.001 |
| Scholarly communication | 0.003 | 0.002 |
| Open science | 0.002 | 0.001 |
| Research integrity | 0.003 | 0.002 |
| Insufficient payload (model declined to judge) | 0.003 | 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".