Safety and efficacy of ginkgo (Ginkgo biloba) during pregnancy and lactation.
Bibliographic record
Abstract
BACKGROUND: There is a lack of basic knowledge on the part of both clinicians and patients as to the indications for use and safety of herbal medicines used in pregnancy and lactation. This is one article in a series that systematically reviews the evidence for commonly used herbs during pregnancy and lactation. OBJECTIVES: To systematically review the literature for evidence on the use, safety, and pharmacology of ginkgo focusing on issues pertaining to pregnancy and lactation. METHODS: We searched 7 electronic databases and compiled data according to the grade of evidence found. RESULTS: There is some very weak scientific evidence from animal and in vitro studies that ginkgo leaf has antiplatelet activity, which may be of concern during labour as ginkgo use could prolong bleeding time. Low-level evidence based on expert opinion shows that ginkgo leaf may be an emmenagogue and have hormonal properties. The safety of ginkgo leaf during lactation is unknown. Patients and clinicians should be aware of past reports of ginkgo products being adulterated with colchicine. CONCLUSIONS: Ginkgo should be used with caution during pregnancy, particularly around labour where its anti-platelet properties could prolong bleeding time. During lactation the safety of ginkgo leaf is unknown and should be avoided until high quality human studies are conducted to prove its safety.
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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 | 0.003 | 0.010 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.001 | 0.001 |
| Bibliometrics | 0.002 | 0.002 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.001 | 0.001 |
| Open science | 0.000 | 0.000 |
| Research integrity | 0.001 | 0.000 |
| Insufficient payload (model declined to judge) | 0.002 | 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".