The efficacy of acupuncture combined with other therapies in post stroke cognitive impairment: A network meta-analysis
Bibliographic record
Abstract
BACKGROUND: The network meta-analysis was used to evaluate the efficacy of acupuncture combined with other therapies in the treatment of post stroke cognitive impairment (PSCI). METHODS: The China National Knowledge Infrastructure, Wanfang DATA, Vip Chinese Periodic Service Platform, PUBMED, Cochrane Library, Web of Science, and Embase were searched for randomized controlled trials (RCTs) published before March 18, 2023. Two researchers independently reviewed articles and extracted data, and then qualified papers were included in the study. STATA 14.0 was used for network meta-analysis. RESULTS: A total of 29 articles including 2241 patients were included in this study. The treatment of the intervention group includes acupuncture combined with traditional Chinese medicine prescriptions (TCMP), acupuncture combined with hyperbaric oxygen (HBO), acupuncture combined with repetitive transcranial magnetic stimulation (rTMS), acupuncture combined with cognitive rehabilitation (CR), acupuncture combined with donepezil. The intervention of the control group includes acupuncture, HBO, rTMS, CR, TCMP, and donepezil. In terms of improving the score of Minimum Mental State Examination (MMSE), acupuncture combined with TCMP was most likely to be the best treatment (P < .05). In terms of improving the score of Montreal Cognitive Assessment (MoCA), acupuncture combined with TCMP was most likely to be the best treatment (P < .05). In terms of improving the total effective rate of clinical treatment, acupuncture combined with rTMS was most likely to be the best treatment (P < .05). CONCLUSION: Acupuncture combined with TCMP may be the best treatment method among all of the above treatments for PSCI.
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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 | 0.023 | 0.046 |
| Meta-epidemiology (narrow) | 0.004 | 0.001 |
| Meta-epidemiology (broad) | 0.017 | 0.056 |
| Bibliometrics | 0.010 | 0.009 |
| Science and technology studies | 0.001 | 0.001 |
| Scholarly communication | 0.003 | 0.002 |
| Open science | 0.002 | 0.002 |
| Research integrity | 0.003 | 0.002 |
| Insufficient payload (model declined to judge) | 0.003 | 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".