<i>EGb</i> in the Treatment for Patients with VCI: A Systematic Review and Meta‐Analysis
Bibliographic record
Abstract
Background . Ginkgo biloba extract (EGb) is widely used to treat impairments in memory, cognition, activities of daily living, inflammation, edema, stroke, Alzheimer’s dementia, and aging. Aim . We aimed to evaluate the safety and efficacy of EGb in treating vascular cognitive impairment (VCI). Methods . The systematic review was performed using the latest guidelines. We searched for EGb‐related trials up to March 1, 2021, in four Chinese databases, three English databases, and clinical trial registry platforms. Randomized controlled trials (RCTs) were included if the study enrolled participants with VCI. Two reviewers independently extracted the data and critically appraised the study quality. Heterogeneity was quantified with I 2 . Both sensitivity and subgroup analyses were used to identify the sources of heterogeneity. Publication bias was assessed with funnel plots. We used the Grading of Recommendations Assessment, Development, and Evaluation (GRADE) approach to rate the evidence quality. Outcomes included assessments using the Activities of Daily Living (ADL), Montreal Cognitive Assessment (MoCA), Mini‐Mental State Examination (MMSE), Hasegawa Dementia Scale (HDS), Barthel Index (BI), Functional Activity Questionnaire (FAQ), and adverse events. Results . In this study, a total of 2019 patients in 23 RCTs were included. EGb appeared to be more effective than control conditions as assessed by the results of cognitive function evaluation, including MMSE (MD MMSE,EGb vs.blank = 3.04, 95% CI: 0.10‐5.98; MD MMSE,EGb vs.drugs for VCI = 2.70, 95% CI: 1.39‐4.01; MD MMSE,EGb+drugs for VCI vs.blank = 5.90, 95% CI: 4.21‐7.59; and MD MMSE,EGb+drugs for VCI vs.drugs for VCI = 3.14, 95% CI: 2.14‐4.15), MoCA (MD MoCA,EGb vs.blank = 5.30, 95% CI: 2.15‐8.46; MD MoCA,EGb+drugs for VCI vs.blank = 2.66, 95% CI: 1.82‐3.50; and MD MoCA,EGb+drugs for VCI vs.drugs for VCI = 2.56, 95% CI: 1.85‐3.27), HDS (MD HDS,EGb vs.blank = 6.50; 95% CI: 4.86‐8.14; MD HDS,EGb+drugs for VCI vs.drugs for VCI = 3.60, 95% CI: 2.50‐4.70), ADL (MD ADL,EGb vs.blank = 7.20, 95% CI: 3.28‐11.12; MD ADL,EGb+drugs for VCI vs.blank = 10.00, 95% CI: 7.51‐12.49; and MD ADL,EGb+drugs for VCI vs.drugs for VCI = 9.20, 95% CI: 7.26‐11.14), BI (MD BI,EGb+drugs for VCI vs.drugs for VCI = 5.71, 95% CI: 2.99‐8.43; MD FAQ,EGb vs.drugs for VCI = −1.43, 95% CI: ‐2.78 to 0.08), and FAQ (MD FAQ,EGb+drugs for VCI vs.drugs for VCI = −2.17, 95% CI: ‐4.13 to 0.21). Evidence of certainty ranged from medium certainty to very low certainty. Conclusion . This meta‐analysis showed that EGb may be an effective and safe treatment in improving MMSE, MOCA, ADL, and BI for VCI patients within three months of diagnosis. However, given the quality of the included RCTs, more preregistered trials are needed that explicitly examine the efficacy of EGb. This systematic review has been registered on PROSPERO, with the registration number CRD42021232967 .
Fetched live from OpenAlex and de-inverted. Abstracts are not stored in this database: the inverted indexes are 8.6 GB of the frame’s 9.3 GB of text, and the host has 13 GB free.
How this classification was reachedexpand
Full frame distilled prediction
Teacher imitationNot calibrated prevalence, not ground truth. Human validation pending. Learned from the 10,348 direct Codex labels and 10,348 direct Gemma labels. Candidate is the union of thresholded teacher heads; consensus is their intersection. These outputs are machine_predicted_unvalidated and are not human labels or direct frontier model labels.
Codex and Gemma teacher scores by category
| Category | Codex | Gemma |
|---|---|---|
| Metaresearch | 0.001 | 0.000 |
| Meta-epidemiology (narrow) | 0.001 | 0.000 |
| Meta-epidemiology (broad) | 0.007 | 0.001 |
| Bibliometrics | 0.000 | 0.001 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.000 | 0.000 |
| Open science | 0.000 | 0.000 |
| Research integrity | 0.000 | 0.000 |
| Insufficient payload (model declined to judge) | 0.000 | 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 teacher head, 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".