Comparative Efficacy of Interferential Therapy and Ultrasound Combined with Knee Exercises vs. TENS with Knee Exercises in Grade 3–4 Knee Osteoarthritis: A Six-Month Randomized Controlled Trial
Bibliographic record
Abstract
Background: Severe knee osteoarthritis (OA), classified as Kellgren-Lawrence (KL) grades 3-4, significantly impairs mobility and quality of life. Non-pharmacological interventions, including electrotherapy modalities, are increasingly utilized to manage symptoms. This study evaluates the comparative efficacy of interferential therapy (IFT) combined with ultrasound (US) and knee exercises versus transcutaneous electrical nerve stimulation (TENS) with knee exercises in patients with advanced knee OA. Methods: A prospective, single-blind, randomized controlled trial was conducted with 50 participants diagnosed with KL grade 3–4 knee OA. Participants were randomized into two groups: Group 1 received IFT and US combined with knee exercises, while Group 2 received TENS with knee exercises. Outcomes assessed at baseline, 3 months, and 6 months included pain (Visual Analog Scale, VAS), function (Western Ontario and McMaster Universities Osteoarthritis Index, WOMAC), knee range of motion (ROM), and quality of life (Short Form-36, SF-36). Results: Group 1 demonstrated statistically significant improvements in WOMAC scores (Δ = 15.2, p < 0.001) and VAS scores (Δ = 3.1, p = 0.002) compared to Group 2 at 6 months. Additionally, ROM and SF-36 physical component scores favored Group 1 (p < 0.05). Conclusion: The combination of IFT and US with knee exercises significantly outperforms TENS with exercises in managing severe knee OA, supporting their integration into rehabilitation 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 | 0.003 | 0.005 |
| Meta-epidemiology (narrow) | 0.002 | 0.001 |
| Meta-epidemiology (broad) | 0.007 | 0.004 |
| Bibliometrics | 0.001 | 0.001 |
| Science and technology studies | 0.001 | 0.002 |
| Scholarly communication | 0.002 | 0.002 |
| Open science | 0.002 | 0.001 |
| Research integrity | 0.004 | 0.003 |
| Insufficient payload (model declined to judge) | 0.008 | 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".