Transcutaneous Electrical Nerve Stimulation and Interferential Current Combined with Exercise for the Treatment of Knee Osteoarthritis: A Randomised Controlled Trial
Bibliographic record
Abstract
Interferential current (IFC) and transcutaneous electrical nerve stimulation (TENS) are forms of electrical stimulation frequently used to treat knee osteoarthritis (OA). The relative effectiveness of these two modalities is currently unknown. The purpose of this study was to evaluate the effects of IFC and TENS, when used in conjunction with exercise, on pain and function in patients with knee OA. Forty-six subjects with radiographically confirmed OA were randomly assigned to one of three groups: TENS and standardised exercises, IFC and exercises or exercises alone. An electrical stimulator was used to apply IFC or TENS at 80 Hz for 20 minutes. All groups had a standardised exercise programme. Treatment was applied twice per week for 4 weeks. Outcomes included a 10-point pain rating scale for pain intensity and the Western Ontario and McMaster University Osteoarthritis Index (WOMAC). A two-way repeated measures ANOVA performed on the pain assessment score showed a statistically significant effect of time (p < 0.001), but not of experimental group (p = 0.813) or interaction (p = 0.067). A similar result was obtained for WOMAC score (p < 0.001, p = 0.241 and p = 0.130 for time, group and interaction effects, respectively). All treatment protocols led to significant improvements in pain and function over time. Neither IFC nor TENS displayed significant additional effects over exercise alone.
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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 | 0.004 |
| Meta-epidemiology (narrow) | 0.001 | 0.001 |
| Meta-epidemiology (broad) | 0.006 | 0.003 |
| Bibliometrics | 0.001 | 0.001 |
| Science and technology studies | 0.001 | 0.002 |
| Scholarly communication | 0.001 | 0.001 |
| Open science | 0.001 | 0.001 |
| Research integrity | 0.003 | 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".