Effect of moxibustion for “nourishing the kidney and benefiting the marrow” on knee osteoarthritis
Bibliographic record
Abstract
ObjectiveTo compare the clinical effect between two acupoint regimens of moxibustion on knee osteoarthritis （KOA）， and observe the influences on the serum content of interleukin 1α （IL-1α）， interleukin-17A （IL-17A）， tumor necrosis factor α （TNF-α）， bone gla protein （BGP） and osteoprotegerin （OPG）.MethodsKOA patients were randomly divided into an observation group （40 cases， 2 cases dropped off） and a control group （40 cases， 3 cases dropped off）. In the observation group， moxibustion was applied to Xiyan （EX-LE5）， Dubi （ST35）， Zusanli （ST36）， Dazhu （BL11）， Xuanzhong （GB39） and Yongquan （KI1） on the affected side. In the control group， EX-LE5， ST35 and ST36 were selected on the affected side. One session of treatment took 30 min in each group， delivered 3 times a week and the duration of treatment was 4 weeks. The scores of Western Ontario and McMaster University （WOMAC） and visual analogue scale （VAS） were observed and the serum content of IL-1α， IL-17A， TNF-α， BGP and OPG of the two groups were measured before and after treatment.ResultsCompared with those before treatment， the WOMAC score， VAS score and the serum content of IL-1α， IL-17A and TNF-α were decreased （P<0.05）， and the content of BGP and OPG were increased （P<0.05） after treatment. Compared with the control group， the WOMAC score， VAS score and the serum content of IL-1α and TNF-α in the observation group were lower （P<0.05）， and the content of BGP and OPG were higher （P<0.05）. The total effective rate of the observation group was 89.5% （34/38）， and that of the control group was 83.8% （31/37）， with no statistically significant difference.ConclusionMoxibustion therapy of “nourishing the kidney and benefiting the marrow” can relieve joint pain， improve joint function， reduce the level of inflammatory factors and ameliorate bone metabolic indicators. The effect of the acupoint regimen in this moxibustion therapy is better than that of the local acupoint selection.
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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 | 0.000 | 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) | 0.001 | 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".