Efficacy and Safety Study of Ultrasound Alone or in Combination with Transcutaneous Electrical Nerve Stimulation for Knee Osteoarthritis: A Retrospective Chart Review
Bibliographic record
Abstract
Objectives This study aimed to compare the therapeutic effects of ultrasound (US) therapy alone versus combined US and transcutaneous electrical nerve stimulation (TENS) in patients with knee osteoarthritis.Methods A retrospective chart review was conducted on 24 patients with knee osteoarthritis who received physical therapy between March and November 2024.Patients were divided into two groups: US therapy alone (n=12) and combined US-TENS therapy (n=12).Treatment outcomes were assessed using the numeric rating scale (NRS), Western Ontario and McMaster Universities osteoarthritis index (WOMAC), and the 36-item short form health survey (SF-36) before and after 8 treatment sessions. ResultsThe combined US-TENS group showed significant improvements in NRS (4.83±0.83 to 2.79±0.86,p<0.001),WOMAC (57.50±26.73 to 33.83±29.95,p=0.001), and physical component summary (PCS) scores (35.55±21.64 to 52.18±25.76,p< 0.001).The US-only group showed no significant changes in any outcome measures.Between-group comparison revealed significant differences in WOMAC (p=0.011) and PCS (p=0.003)improvements favoring the combined therapy group.Conclusions Combined US-TENS therapy showed superior effectiveness compared to US therapy alone in improving knee function and physical quality of life in patients with knee osteoarthritis.However, neither treatment significantly improved mental health outcomes, suggesting the need for additional psychological interventions in managing chronic knee pain.(
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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 | 0.004 | 0.011 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.001 | 0.001 |
| Bibliometrics | 0.002 | 0.002 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.001 | 0.001 |
| Open science | 0.000 | 0.000 |
| Research integrity | 0.000 | 0.000 |
| Insufficient payload (model declined to judge) | 0.001 | 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".