Efficacy of Non-Invasive Radiofrequency-Based Diathermy in the Postoperative Phase of Knee Arthroplasty: A Double-Blind Randomized Clinical Trial
Bibliographic record
Abstract
Total knee replacement (TKR) surgery ameliorates knee function and the quality of life of patients, although 20% still experience dissatisfaction due to pain limiting their function. Radiofrequency Diathermy (MDR) has shown improvements in knee osteoarthritis and patellofemoral pain syndrome. As such, this study aims to assess the effects of MDR in the postoperative treatment of TKR patients. Forty-two participants were allocated to an experimental, placebo, or control group. For two weeks, subjects performed daily knee exercises and MDR, knee exercises and placebo MDR, or only knee exercises. Data from the Visual Analogue Scale (VAS), Timed Up-and-Go (TUG) test, Five Times Sit-to-Stand Test (FSST), Western Ontario and McMaster Universities Arthritis Index (WOMAC), physical component summary (PCS), and the mental component summary (MCS) of the SF-12 questionnaire were collected. Group-by-time interaction was significant, with favorable results in the MDR group for VAS (p = 0.009) and WOMAC (p = 0.021). No significant differences were found for TUG, FSST, PCS, or MCS (p > 0.05). In conclusion, the addition of MDR to therapeutic knee exercises obtained better results for knee pain than exercise alone in patients who had recently undergone TKR surgery.
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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 | 0.002 | 0.002 |
| Meta-epidemiology (narrow) | 0.001 | 0.001 |
| Meta-epidemiology (broad) | 0.004 | 0.002 |
| Bibliometrics | 0.001 | 0.001 |
| Science and technology studies | 0.001 | 0.001 |
| Scholarly communication | 0.001 | 0.001 |
| Open science | 0.001 | 0.000 |
| Research integrity | 0.003 | 0.002 |
| Insufficient payload (model declined to judge) | 0.007 | 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".