Meta-analysis of Acupuncture Efficacy on Nerve Root Cervical Spondylosis Therapy
Bibliographic record
Abstract
Objective:To systematically review the efficacy and safety of acupuncture in the therapy of cervical spondylotic myelopathy.Methods:Randomized controlled trials (RCT) about acupuncture therapy on nerve root type cervical spondylosis were electronically searched in CNKI, PubMed, EMBase, Wanfang Data. After conducting quality evaluation according to the"bias risk assessment tool"in the Cochrane Collaboration, the results of Meta analysis were analysed by RevMan 5.3 software.Results:A total of 11 RCTs involved 974 patients were finally included. Meta analysis indicated that acupuncture combined with traction in the therapy of nerve root type cervical to improve spondylopathy pain relief [MD=-1.69, 95%CI (-2.63, -0.75) ];to improve the current pain conditions (PPI) [MD=-0.41, 95%CI (-0.56, -0.26) ];pain relief [VAS (McGill) ] [MD=-1.43, 95%CI (-2.21, -0.6) ];total pain score (PRI) [MD=-2.43, 95%CI (-4.06, -0.80) ];pain perception total score [PRI (S) ] [MD=-1.63, 95%CI (-2.07, -1.20) ];Pain Emotion Total Score [PRI (A) ] [MD=-0.89, 95%CI (-1.39, -0.38) ].The effect of acupuncture group in cervical spondylotic myelopathy therapy was better than that of the traction group, the difference was statistically significant (P<0.05).Conclusion:Acupuncture has good curative effect and advantage in the therapy of cervical spondylosis of nerve root type. However, there are many deficiencies in low-quality inclusion studies and heterogeneity between studies in this systematic review, large-sample, high-quality randomized controlled trials are necessary for validation in the future.
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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 | 0.019 | 0.048 |
| Meta-epidemiology (narrow) | 0.004 | 0.002 |
| Meta-epidemiology (broad) | 0.028 | 0.048 |
| Bibliometrics | 0.008 | 0.007 |
| Science and technology studies | 0.001 | 0.001 |
| Scholarly communication | 0.004 | 0.002 |
| Open science | 0.002 | 0.002 |
| Research integrity | 0.003 | 0.002 |
| Insufficient payload (model declined to judge) | 0.004 | 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".