The Efficacy of Cupping Therapy Added to Electroacupuncture and Exercise Therapy on Knee Osteoarthritis
Bibliographic record
Abstract
Background: Electroacupuncture and exercise therapy have been used to treat knee osteoarthritis, but evidence for adding cupping to this treatment is lacking. Therefore, this study aimed to investigate the effect of cupping and acupuncture combined with exercise on knee osteoarthritis. Materials and Methods: This randomized control trial was done on 56 patients with knee osteoarthritis. We had two groups: a control and an intervention group. Both groups received electroacupuncture and exercise therapy programs. The intervention group received cupping after electroacupuncture plus exercise therapy. The Western Ontario and McMaster Universities Index (WOMAC) questionnaire and Visual Analogue Scale (VAS) measured patient outcomes before and after treatment. Results: All patients' VAS and WOMAC scores decreased in these two groups after treatment. The difference between VAS and WOMAC scores and pain and knee function was significant compared to the intervention group with the control group (p<0.05). The difference in knee stiffness was not significant comparing the intervention group with the control group (p>0.05). Conclusion: Adding cupping therapy following electroacupuncture and exercise therapy significantly decreased pain and improved function.
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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 | 0.000 |
| Meta-epidemiology (broad) | 0.002 | 0.002 |
| Bibliometrics | 0.001 | 0.000 |
| Science and technology studies | 0.000 | 0.001 |
| Scholarly communication | 0.001 | 0.000 |
| Open science | 0.001 | 0.001 |
| 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".