Effects of Warm Needle Acupuncture plus Xitong Waixi Lotion in Patients with Knee Osteoarthritis: A Randomized Controlled Trial
Post-publication record
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Bibliographic record
Abstract
Objective. To evaluate the effect of warm needle acupuncture plus Xitong Waixi lotion on the levels of IL-1, TNF-α, and MMP-3 in patients with knee osteoarthritis. Methods. Eighty patients with knee osteoarthritis admitted to our hospital from October 2019 to June 2021 were recruited and assigned via the random number table method at a ratio of 1 : 1 to receive either Xitong Waixi lotion (conventional group) or warm needle acupuncture plus Xitong Waixi lotion (combined group). Outcome measures included clinical efficacy, inflammatory cytokine level, Western Ontario and McMaster Universities Arthritis Index (WOMAC) score, visual analogue scale (VAS) score, Hospital for Special Surgery (HSS) knee score, and adverse reactions. Results. Warm needle acupuncture plus Xitong Waixi lotion was associated with a significantly higher clinical efficacy versus Xitong Waixi lotion alone ( P = 0.006 ). Patients in the combined group had significantly lower levels of interleukin (IL)-1, tumor necrosis factor-α (TNF-α), and matrix metalloproteinase-3 (MMP-3) than those in the conventional group ( P = 0.020 ). Warm needle acupuncture plus Xitong Waixi lotion resulted in significantly lower WOMAC scores and VAS scores and higher HSS scores for the patients versus Xitong Waixi lotion ( P = 0.012 ). The two groups had a similar incidence of adverse events ( P = 0.068 ). Conclusion. Warm needle acupuncture plus Xitong Waixi lotion effectively alleviates the inflammatory response and knee pain in patients with knee osteoarthritis, with significant clinical effects and a high safety profile.
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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 | 0.001 |
| Meta-epidemiology (broad) | 0.005 | 0.003 |
| 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 | 0.002 |
| Insufficient payload (model declined to judge) | 0.006 | 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".