Clinical Observation of Alternative Wave Electroacupuncture Combined with Lee’s Naprapathy in Treating Knee Osteoarthritis (Blood Stasis due to Qi Stagnation)
Post-publication record
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Bibliographic record
Abstract
Objective. We aim to explore the clinical therapeutic effect of alternative wave electroacupuncture combined with Lee’s naprapathy therapy on knee osteoarthritis (KOA) (blood stasis due to qi stagnation). Method. 122 patients with KOA treated in our hospital from January 2018 to October 2021 were randomly grouped into a combined group (n = 61) and a control group (n = 61). The combined group was treated with alternative wave electroacupuncture combined with Lee’s naprapathy, while the control group was treated with alternative wave electroacupuncture alone. Clinical efficacy of the two groups was observed. The Visual Analogue Scale (VAS), Lysholm Scale, Indexes of Severity for Osteoarthritis (ISOA), and Western Ontario and McMaster Universities Osteoarthritis Index (WOMAC) were compared before and after treatment, followed up for 3 months and 6 months. The adverse reactions of the two groups were observed. Result. The overall response rate of the combined group (96.72%) was higher than that of the control group (81.97%), and the difference was statistically significant ( P < 0.05 ). After treatment and follow-up for 3 months and 6 months, the Lysholm score of the combined group was higher than that of the control group, while the VAS, ISOA, and WOMAC scores were lower than those of the control group, and the difference between the two was statistically significant ( P < 0.05 ). There were no serious adverse reactions in both groups ( P > 0.05 ). Conclusion. The alternative wave electroacupuncture combined with Lee’s naprapathy is effective and safe in treating KOA (blood stasis due to qi stagnation).
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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 | 0.000 | 0.000 |
| Insufficient payload (model declined to judge) | 0.001 | 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".