A network meta-analysis of different acupuncture therapy in the treatment of poststroke cognitive impairment and dementia
Bibliographic record
Abstract
BACKGROUND: Poststroke cognitive impairment and dementia (PSCID) is a major cause of stroke-related morbidities and mortalities. Over the last few years, there has been growing evidence supporting the effectiveness of needle-related treatments in PSCID. Our goal was to rate the included therapies and assess the clinical effectiveness of various needle-related treatments in patients with PSCID. METHODS: We searched PubMed, Web of Science, Cochrane Library, Embase, China National Knowledge Infrastructure (CNKI), China Science and Technology Journal Database (VIP), Chinese Biomedical Literature Service System (SinoMed), Wanfang, FDA.gov, and ClinicalTrials.gov. A mix of subject terms and free words was used to search the databases. The retrieval period was from the inception date of the database to February, 2023. We included SRs and MAs from acupuncture RCTs of patients with PSCID. The Cochrane Risk Assessment Scale was used to evaluate the risk of bias in the included studies. State 17.0 was used for network meta-analysis in accordance with the Bayesian framework. RESULTS: There were 34 studies total of 2690 patients. The cumulative ranking curve (SUCRA) revealed that CT + CFT + EA was the most efficient intervention to improve (Mini-Mental State Examination, MMSE) efficiency, followed by CT + CFT + AP to improve (Montreal Cognitive Assessment, MoCA) efficiency, CT + CFT + ACU for improving (Activities of Daily Living scale, ADL) scores, and CT + CFT + EA to improve clinical efficiency. CONCLUSION: The results show that Different acupuncture methods can improve cognitive function and daily living ability in patients with PSCID. Network meta-analysis revealed that both CT + CFT + ACU and CT + CFT + EA appeared to be more beneficial for daily living activities, while CT + CFT + EA and CT + CFT + AP appeared to be more helpful for cognitive performance in patients with PSCID. Treatments including acupuncture are safer and have a reduced incidence of negative side effects.
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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 | 0.052 |
| Meta-epidemiology (narrow) | 0.004 | 0.001 |
| Meta-epidemiology (broad) | 0.018 | 0.060 |
| Bibliometrics | 0.008 | 0.006 |
| 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) | 0.005 | 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".