[Efficacy evaluation on knee osteoarthritis treated with acupuncture: non-randomized concurrent control trial].
Bibliographic record
Abstract
OBJECTIVE: To evaluate the clinical efficacy and efficacy sustainable time of acupuncture in knee osteoarthritis (KOA). METHODS: The non-randomized concurrent control trial was adopted. One hundred and ninety-three cases of KOA were divided into an immediate acupuncture group (group A, 97 cases) and a delayed acupunc-weeks at the end of treatment. In group B, the same acupuncture therapy was applied after waiting 4 weeks. The acupoints in the two groups were Liangqiu (ST 34), Dubi (ST 35), Zusanli (ST 36), Yanglingquan (GB 34), Yinlingquan (SP 9), Xuehai (SP 10), Xiyan (EX-LE 4), Xiyangguan (GB 33). WOMAC (Western Ontario and McMasters Universities Osteoarthritis) was used for the assessment of the primary index and VAS (visual analogue scale) was for the secondary index. The evaluation was accomplished by the patients at the beginning of trial, on the 4th and 8th weeks. In each group, 72 patients finished the trial and the data of the lost cases were included in the final data analysis. RESULTS: In the 4th week of trial, WOMAC score was (25. 8+/-22.0) in group A difference (P<0. 001). VAS scorewas (31. 8+/-24. and was (43.8+/-22.2) in group B, indicating the significant 6) in group A and was (56. 6 +/-25. 8) in group B, indicating very significant difference (P<0. 001). In the 8th week, the efficacy was reduced slightly in the follow-up of group A, but it was improved apparently as compared Acupuncture relieves joint pain and improves joint function obviously.by th patiĩeffr,a Mtaetfti-?an tf ri-with that before treatment. CONCLUSION: Acupuncture relieves joint pain and improves joint function obviously.The effect of acupuncture is still sustainable in 4 weeks after terminating the treatment.
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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 | 0.004 | 0.004 |
| Meta-epidemiology (narrow) | 0.001 | 0.001 |
| Meta-epidemiology (broad) | 0.004 | 0.002 |
| Bibliometrics | 0.001 | 0.001 |
| Science and technology studies | 0.001 | 0.002 |
| Scholarly communication | 0.001 | 0.001 |
| Open science | 0.001 | 0.001 |
| Research integrity | 0.002 | 0.002 |
| Insufficient payload (model declined to judge) | 0.005 | 0.001 |
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".