Safety of Chinese herbal medicines during pregnancy
Bibliographic record
Abstract
Miscarriage and infertility have long been public concerns due to the mental and physical suffering they bring to potential parents. There is a strong need for effective and affordable treatments. Chinese herbal medicines (CHMs) have been shown to be effective for preventing miscarriage and treating infertility; however, due to the limited knowledge of their pharmacological mechanisms and unknown potential toxicity, their use has been restricted. This paper reviews 24 clinical trials of CHMs to prevent miscarriage and treat infertility. Most of these studies did not meet the requirements of randomized controlled trials. Even when using quality assessments based on the Newcastle-Ottawa Scale to assess the quality of non-randomized studies, most studies did not meet the requirements. The reviewed papers were evaluated for maternal and embryonic adverse effects, including those in animal experiments. Slight maternal effects were noted, with some reports of severe toxic effects of CHMs for preventing miscarriage and severe adverse maternal effects of CHMs used for infertility. Owing to the poor quality of the randomized controlled clinical trials and the limited number of studies, it is not possible to draw a conclusion. From animal studies, for all three gestational periods, growth delay and congenital anomalies were the most commonly recorded adverse effects. However, baseline toxicological data and detailed mechanisms are still lacking. To gain a better understanding of the potential toxic effects of CHMs, additional high-quality randomized controlled trials should be conducted, and high-throughput in vitro screening method for baseline data should be considered.
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 distilled prediction
Teacher imitationNot calibrated prevalence, not ground truth. Human validation pending. Learned from the 10,348 direct Codex labels and 10,348 direct Gemma labels. Candidate is the union of thresholded teacher heads; consensus is their intersection. These outputs are machine_predicted_unvalidated and are not human labels or direct frontier model labels.
Codex and Gemma teacher scores by category
| Category | Codex | Gemma |
|---|---|---|
| Metaresearch | 0.001 | 0.000 |
| Meta-epidemiology (narrow) | 0.001 | 0.000 |
| Meta-epidemiology (broad) | 0.005 | 0.001 |
| Bibliometrics | 0.000 | 0.000 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.000 | 0.000 |
| Open science | 0.001 | 0.000 |
| Research integrity | 0.001 | 0.001 |
| Insufficient payload (model declined to judge) | 0.000 | 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 teacher head, 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".