Electroacupuncture with different current intensities to treat knee osteoarthritis: a single-blinded controlled study.
Bibliographic record
Abstract
BACKGROUND: To assess the efficacy of Electroacupuncture (EA) stimulation with high-intensity compared with low-intensity on knee osteoarthritis (KOA). METHODS: Participants with KOA were randomized to either high-intensity EA group or low-intensity EA group. EA was applied unilaterally on the affected leg with the local points GB34, ST34, EX-LE4, EX-LE5, ST36, and SP9. The visual analogue scale (VAS) and the Western Ontario and McMaster Universities Osteoarthritis Index (WOMAC) were measured before and after participation. Plasma TNFα, IL-1β, IL-6, and apelin levels were also assessed by enzyme immunoassay (ELA) before and after treatment. RESULTS: Of 80 participants who consented to study participation, 77 completed the program. The patients showed a significant improvement in their pain, stiffness, and physical function on the VAS and WOMAC, accompanying with a significantly reduction in plasma levels of apelin and TNFα. Furthermore, high-intensity group exhibited statistically significant improvements in stiffness and physical function symptoms compared with low-intensity group. Plasma level of IL-6 was significantly decreased only after high-intensity EA treatment. Furthermore, apelin level was significantly inhibited in high-intensity EA group than in low-intensity EA group. CONCLUSIONS: Both high- and low-intensity EA treatments alleviate the clinical symptoms of KOA patients. High-intensity EA is more effective than low-intensity EA. Changes in plasma levels of TNFα, apelin and IL-6 may be involved in the therapeutic effect of EA on KOA.
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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 | 0.002 |
| Meta-epidemiology (narrow) | 0.001 | 0.001 |
| Meta-epidemiology (broad) | 0.004 | 0.001 |
| 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 | 0.002 | 0.001 |
| Insufficient payload (model declined to judge) | 0.004 | 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".