Efficacy of action potential simulation and interferential therapy in the rehabilitation of patients with knee osteoarthritis
Bibliographic record
Abstract
OBJECTIVE: Knee osteoarthritis (OA) is the main cause of pain, physical impairment and chronic disability in older people. Electrotherapeutic modalities such as interferential therapy (IFT) and action potential simulation (APS) are used for the treatment of knee OA. In this study, we aim to evaluate the therapeutic effects of APS and IFT on knee OA. METHODS: In this randomized clinical trial, 67 patients (94% female and 6% male with mean age of 52.80 ± 8.16 years) with mild and moderate knee OA were randomly assigned to be treated with APS (n = 34) or IFT (n = 33) for 10 sessions in 4 weeks. Baseline and post-treatment Western Ontario and McMaster Universities Osteoarthritis (WOMAC) subscales, visual analogue scale (VAS) and timed up and go (TUG) test were measured in all patients. RESULTS: VAS and WOMAC subscales were significantly improved after treatment in APS and IFT groups (p < 0.001 for all). TUG was also significantly improved after treatment in APS group (p < 0.001), but TUG changes in IFT was not significant (p = 0.09). There was no significant difference in VAS, TUG and WOMAC subscales values before and after treatment as well as the mean improvement in VAS, TUG and WOMAC subscales during study between groups. CONCLUSION: Short-term treatment with both APS and IFT could significantly reduce pain and improve physical function in patients with knee OA.
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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 | 0.001 | 0.001 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.001 | 0.001 |
| Bibliometrics | 0.000 | 0.000 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.000 | 0.000 |
| Open science | 0.000 | 0.000 |
| Research integrity | 0.001 | 0.001 |
| 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".