Therapeutic efficacy of the Ginkgo special extract EGb761 <sup>®</sup> within the framework of the mitochondrial cascade hypothesis of Alzheimer’s disease
Bibliographic record
Abstract
Objectives: The mitochondrial cascade hypothesis of dementia assumes mitochondrial dysfunction as an important common pathomechanism for the whole spectrum of age-associated memory disorders from cognitive symptoms in the elderly over mild cognitive impairment to Alzheimer’s dementia. Thus, a drug such as the Ginkgo special extract EGb 761® which improves mitochondrial function should be able to ameliorate cognitive deficits over the whole aging spectrum.Methods: We review the most relevant publications about effects of EGb 761® on cognition and synaptic deficits in preclinical studies as well as on cognitive deficits in man from aging to dementia.Results: EGb 761® improves mitochondrial dysfunction and cognitive impairment over the whole spectrum of age-associated cognitive disorders in relevant animal models and in vitro experiments, and also shows clinical efficacy in improving cognition over the whole range from aging to Alzheimer’s or even vascular dementia.Conclusions: EGb 761® shows clinical efficacy in the treatment of cognitive deficits over the whole spectrum of age-associated memory disorders. Thus, EGb 761® can serve as an important pharmacological argument for the mitochondrial cascade hypothesis of dementia.
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 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 | 0.000 |
| Meta-epidemiology (narrow) | 0.001 | 0.000 |
| Meta-epidemiology (broad) | 0.001 | 0.000 |
| Bibliometrics | 0.002 | 0.001 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.001 | 0.001 |
| Open science | 0.001 | 0.001 |
| Research integrity | 0.001 | 0.001 |
| Insufficient payload (model declined to judge) | 0.003 | 0.001 |
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".