Comparison of multiple acupoints combination in the treatment of post-stroke cognitive impairment: A network meta-analysis
Bibliographic record
Abstract
BACKGROUND: To evaluate the efficacy of multiple acupoint combinations for the treatment of post-stroke cognitive impairment (PSCI) using a network meta-analysis method. METHODS: Searches for clinical randomized controlled trials (RCTs) of various types of acupuncture treatments for post-stroke cognitive dysfunction were conducted, data were extracted from studies selected according to the inclusion criteria, and the RCTs included in the analysis were assessed separately for risk of literature bias. Network meta-analysis was performed using Stata 14.0. RESULTS: Sixteen RCTs involving 1257 patients were included, which involved 9 groups of acupoint treatment plans. The best treatment plan for improving the mini-mental state examination score of PSCI was a cephalic plexus spur (99.7%). The best treatment option for improving the montreal cognitive assessment score for PSCI was Zishen Yisui acupuncture therapy (ZSYSA) (77.3%). The best option for improving the barthel index score of PSCI was ZSYSA (99.2%). In terms of improving the overall clinical outcomes of PSCI, the best treatment option for improving the overall clinical effectiveness of PSCI is ZSYSA Therapy (92.2%). CONCLUSION: The analysis of all results shows that ZSYSA can significantly improve PSCI compared to other acupuncture therapies. STRENGTHS AND LIMITATIONS OF THIS STUDY: This is the 1st study on the treatment of PSCI with different acupoint combinations based on a network meta-analysis method, which provides a reference for clinical rehabilitation workers; all included studies were randomized controlled trials, which increased the reliability of this study. Limitations; The number of relevant clinical studies retrieved was too small, and all included clinical trials were located in China; therefore, there is a great possibility of publication bias; Most of the included studies did not clearly explain the random distribution mode, follow-up, distribution concealment, or other experimental conditions. Therefore, selection and reporting biases cannot be excluded, suggesting that the quality of the literature is not high; Because of the strict inclusion criteria, the number of studies was limited, and subgroup analysis could not be performed according to the time of onset and the length of the disease course.
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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 | 0.026 | 0.040 |
| Meta-epidemiology (narrow) | 0.004 | 0.001 |
| Meta-epidemiology (broad) | 0.016 | 0.053 |
| Bibliometrics | 0.009 | 0.006 |
| Science and technology studies | 0.001 | 0.001 |
| Scholarly communication | 0.003 | 0.002 |
| Open science | 0.003 | 0.002 |
| Research integrity | 0.002 | 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".