Ginkgo biloba Efficacy in the Treatment of Drug-Induced Parkinsonism: A Randomized Clinical Trial
Bibliographic record
Abstract
Background: Drug-induced parkinsonism (DIP) is one of the most common movement disorders in approximately 20 - 35% of patients on antipsychotic medications. Managing the symptoms of DIP is challenging due to the limited number of potentially effective medications. On the other hand, this restricted possible treatment could have numerous side effects that ultimately result in patients stopping the medication all at once. The neuroprotective property of Ginkgo biloba extract (EGb) emerged as an effective commodity for the additional treatment of psychiatric disorders. Objectives: This study aimed to evaluate the efficacy of EGb in psychiatric patients with symptoms of DIP. Methods: A sample of 63 patients who met the inclusion criteria were recruited and randomly assigned to control and experimental groups. Both groups were followed for 3 months. One group received 80 mg of G. biloba three times a day, and the control group received a placebo. The patients were evaluated using the Unified Parkinson’s Disease Rating Scale and Montreal Cognitive Assessment. Results: Ginkgo could change the intensity of rest tremors, the severity of motor symptoms, rigidity, and bradykinesia. Ginkgo biloba might alleviate the severity of parkinsonism and motor symptoms and could lead to changes in the two components of working memory and short-term memory. Conclusions: Ginkgo biloba extract can be used as an effective and safe treatment in the management of DIP, whether in patients diagnosed with psychotic disorders or mood disorders.
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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 | 0.002 | 0.001 |
| Meta-epidemiology (narrow) | 0.001 | 0.000 |
| Meta-epidemiology (broad) | 0.004 | 0.002 |
| Bibliometrics | 0.001 | 0.001 |
| Science and technology studies | 0.000 | 0.001 |
| Scholarly communication | 0.001 | 0.001 |
| Open science | 0.001 | 0.000 |
| Research integrity | 0.002 | 0.002 |
| Insufficient payload (model declined to judge) | 0.006 | 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".