Ginkgo biloba extract for essential hypertension: A systemic review
Bibliographic record
Abstract
BACKGROUND: Ginkgo biloba extract (GBE), a traditional natural herbal product, is often used in the treatment of essential hypertension (EH) as complementary therapy in China and European countries. AIM: To critically assess the current clinical evidence of efficacy and safety of GBE for EH. METHODS: 7 electronic databases (Cochrane Library, PubMed, EMBASE, VIP, CBM, Wanfang data, and CNKI) were searched to identify randomized controlled trials (RCTs) of GBE for EH. Methodological quality was assessed independently using the Cochrane Handbook for Systematic Reviews of Interventions. RESULTS: A total of 9 RCTs with 1012 hypertensive patients were identified and reviewed. Most RCTs were of high risk of bias with flawed study design and poor methodological quality. 6 trials demonstrated potential positive effect of GBE as complementary therapy on BP reduction when compared with antihypertensive drug therapy; however, it was not associated with a statistically significant effect on both SBP and DBP reduction in 3 other trials. Despite the positive findings, there were so many methodological limitations and significant clinical heterogeneity. Most of the trials did not report adverse effects, and the safety of GBE is still uncertain. CONCLUSION: No confirmative conclusions on the efficacy and safety of GBE for EH could be drawn. More rigorous trials are warranted to support their clinical use.
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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 | 0.005 |
| Meta-epidemiology (narrow) | 0.001 | 0.001 |
| Meta-epidemiology (broad) | 0.004 | 0.003 |
| Bibliometrics | 0.006 | 0.005 |
| Science and technology studies | 0.000 | 0.001 |
| Scholarly communication | 0.002 | 0.001 |
| Open science | 0.001 | 0.001 |
| Research integrity | 0.001 | 0.001 |
| Insufficient payload (model declined to judge) | 0.005 | 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".