Effectiveness and safety of ginkgo biloba preparations in the treatment of Alzheimer's disease: A systematic review and meta-analysis
Bibliographic record
Abstract
Objective To conduct a meta-analysis of the effectiveness and safety of ginkgo biloba preparations combined with donepezil hydrochloride vs. donepezil for the treatment of Alzheimer's disease (AD). Methods Three English databases (Cochrane Library, PubMed, EMBASE), and four Chinese databases [the China National Knowledge Infrastructure (CKNI), the Chinese Biomedical Literature database (CBM), the Chongqing VIP database, and WANFANG DATA)] were manually searched for literature published from the respective dates of inception of the databases to December 2022. The randomized controlled trials (RCTs) of ginkgo biloba preparations with donepezil hydrochloride vs. donepezil for the treatment of AD were included. Relevant literature was screened, and the data in the included studies were extracted for quality assessment according to the Risk of bias tool. The RevMan 5.3 software was used for meta-analysis. Results A total of 1,642 participants were enrolled in the 18 RCTs. Of these, 842 were in the experimental group (ginkgo biloba preparations combined with donepezil hydrochloride) and 800 were in the control group (donepezil). The overall methodological quality of the included RCTs is poor due to the high risks of blindness and allocation concealment. The meta-analysis results showed statistically significant differences in several outcomes including Risk Ratio (RR) in change for clinical effectiveness rate (1.23, 95% CI 1.13, 1.34, P < 0.00001), mean difference (MD) in change for Mini-Mental State Examination score (3.02, 95% CI 2.14, 3.89, P < 0.00001), Activity of Daily Living Scale score (−4.56, 95% CI −5.09, −4.03, P < 0.00001), Hasegawa Dementia Scale score (2.04, 95% CI 1.74, 2.34, P < 0.00001), Montreal Cognitive Assessment score (2.38, 95% CI 0.72, 4.06, P = 0.005), between the experimental and control groups. But there is no statistically significant difference in change for adverse reaction (0.91, 95% CI 0.58, 1.42, P = 0.69). Conclusion Ginkgo biloba preparations plus donepezil can improve clinical effectiveness rate and vocabulary memory outcomes. However, more relevant high-quality RCTs are needed in the future to validate these results. Systematic review registration Identifier CRD42022378970.
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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 | 0.025 | 0.038 |
| Meta-epidemiology (narrow) | 0.004 | 0.002 |
| Meta-epidemiology (broad) | 0.029 | 0.053 |
| Bibliometrics | 0.009 | 0.007 |
| Science and technology studies | 0.001 | 0.001 |
| Scholarly communication | 0.004 | 0.002 |
| Open science | 0.003 | 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".