Therapeutic Evaluation of the Accumulative Analgesic Effect of Electroacupuncture
Bibliographic record
Abstract
Objective To observe the accumulative analgesic effect of electroacupuncture.Method Sixty patients with sciatica were randomized into electroacupuncture-analgesia(EA) group(n=30) and mind-tranquilizing(MR) group(n=30).In EA group,Zusanli(ST36),Yanglingquan(GB34),Huantiao(GB30),Weizhong(BL40),Shenshu(BL23),and Dachangshu(BL25) were selected.In MR group,Zusanli(ST36),Yanglingquan(GB34),Huantiao(GB30),Weizhong(BL40),Shenshu(BL23),Dachangshu(BL25),Baihui(GV20),and Sishencong(EX-HN1) were selected.Therapeutic efficacy was determined by using short-form McGill Pain Questionnaire,and Pain Rating Index(PRI) was also used for evaluation before treatment,after 2-day treatment and at the end of the treatment course.Result After 2-day treatment,the sensory and emotion scores didn’t show obvious changes in the two groups(P0.05);after 1 treatment course,the sensory and emotion scores were significantly reduced compared to those before treatment in both groups(P0.05),and the PRI score in MR group was significantly lower than that in EA group(P0.05).Conclusion Repeated electroacupuncture treatment can produce an accumulative analgesic effect,and mind tranquilization(improvement of memory) can enhance the accumulative analgesic effect of acupuncture to some extent.
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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) | 0.002 | 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".