Portable Transcutaneous Electrical Nerve Stimulation Therapy at Different Frequencies in the Treatment of Knee Osteoarthritis: A Quasi-Experimental Study.
Bibliographic record
Abstract
INTRODUCTION: The prevalence of knee osteoarthritis is on the raise. This raise has been a huge financial burden to developed countries in treating the disease. Transcutaneous electrical nerve stimulation (TENS) is a cost-effective, easily available, and self-applicable mode of non-pharmacological pain relieve technique. Despite these advantages, the use, settings, and effectiveness of portable TENS are still poorly understood. The aim of this study is to determine the effectiveness of portable TENS at different frequencies in treating knee osteoarthritis. MATERIALS AND METHODS: This is a single-center quasi-experimental study involving 100 patients seen in the outpatient department with knee osteoarthritis. They were randomly (computer generated) allocated into two arms (high frequency [H-F] or low frequency [L-F]). H-F is set at 100 Hz and L-F is set at 4 Hz. A baseline assessment is taken with the visual analog score (VAS), Western Ontario and McMaster Universities Osteoarthritis Index (WOMAC), Oxford Knee Score, and Lequesne index. They were instructed to self-administer the TENS therapy as per protocol and followed up at the 4th and 12th week to be reevaluated on the above scores. RESULTS: The final results show that both H-F and L-F groups showed improvement in all parameters of the VAS, WOMAC index, Oxford Knee Score, and Lequesne index (73%). Only the pain component of Lequesne index, activities of daily living component of Lequesne index, total Lequesne index, and pain component of WOMAC index shows a statistically significant difference, favoring the H-F group. The H-F group yields a faster result; however, with time the overall effect remains the same in both groups. CONCLUSION: Both H-F and L-F groups show improvement in all the component of Lequesne index, Oxford Knee Score, WOMAC index, and VAS with no statistical difference between the two groups. Although H-F yields a faster result, not everyone is able to tolerate the intensity. Therefore, the selection of H-F or L-F should be done on case basis depending on the severity of symptoms, patient's expectation, and patient's ability to withstand the treatment therapy. Based on this 12th week follow-up, both groups will continue to improve with time. A longer study should be conducted to see it this improvement will eventually plateau off or continue to improve until the patient is symptom free.
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How this classification was reachedexpand
Full frame distilled prediction
Teacher imitationNot calibrated prevalence, not ground truth. Human validation pending. Learned from the 10,348 direct Codex labels and 10,348 direct Gemma labels. Candidate is the union of thresholded teacher heads; consensus is their intersection. These outputs are machine_predicted_unvalidated and are not human labels or direct frontier model labels.
Codex and Gemma teacher scores by category
| Category | Codex | Gemma |
|---|---|---|
| Metaresearch | 0.000 | 0.000 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| 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 | 0.000 | 0.000 |
| Insufficient payload (model declined to judge) | 0.000 | 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 teacher head, 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".