Ginkgo Biloba Extract (EGb 761) in Alzheimers Disease: Is there Any Evidence?
Bibliographic record
Abstract
For centuries, extracts from the leaves of the Ginkgo biloba tree have been used as Chinese herbal medicine to treat a variety of health disorders. The standardized Ginkgo biloba extract EGb 761 was marketed in France and Germany 30 years ago for various vascular and cerebral deficits and is now classified as a food supplement in the United States. EGb 761 is currently the focus of phase-III clinical trials, GEM and GuidAge studies, to evaluate its efficacy on the prevention of Alzheimer's disease (AD) in subjects over 70 years old. This review summarizes recent advancements in our understanding of the potential role of EGb 761 in the prevention of AD. Besides its well-known free radical scavenging properties, the ability of EGb 761 to protect neurons probably also involves other intracellular pathways. We will point out potential targets of EGb 761 in the amyloid cascade such as its antiamyloidogenic properties or the regulation of gene expression. Moreover we will discuss the complexity of the cellular and molecular mechanisms of EGb 761 and the significance of the synergic effect of different constituents of EGb 761.
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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 | 0.000 | 0.001 |
| Meta-epidemiology (narrow) | 0.001 | 0.000 |
| Meta-epidemiology (broad) | 0.002 | 0.000 |
| Bibliometrics | 0.001 | 0.002 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.001 | 0.001 |
| Open science | 0.001 | 0.000 |
| Research integrity | 0.001 | 0.001 |
| Insufficient payload (model declined to judge) | 0.004 | 0.002 |
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".