Systematic Review of Breastfeeding and Herbs
Bibliographic record
Abstract
OBJECTIVES: Despite popular and historical use, there has been little modern research conducted to determine the safety and efficacy of herb use during breastfeeding. The purpose of this study was to systematically review the clinical literature on herbal medicine and lactation. METHODS: The databases PubMed, CAB Abstracts, Cochrane Central Register of Controlled Trials, HealthSTAR, Cumulative Index to Nursing and Allied Health Literature, and Reprotox were systematically searched for human trials from 1970 until 2010. Reference lists from relevant articles were hand-searched. RESULTS: Thirty-two studies met the inclusion criteria. Clinical studies were divided into three categories: survey studies (n=11), safety studies (n=8), and efficacy studies (n=13). Six studies were randomized controlled trials. The most common herbs studied were St. John's wort (Hypericum perforatum L.) (n=3), garlic (Allium sativum L.) extract (n=2), and senna (Cassia senna L.) (n=2). Studies were very heterogeneous with regard to study design, herbal intervention, and outcome measures. Overall, poor methodological quality predominated among the studies. CONCLUSIONS: Our review concludes that further research is needed to assess the prevalence, efficacy, and safety of commonly used herbs during breastfeeding.
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 | 0.007 | 0.030 |
| Meta-epidemiology (narrow) | 0.002 | 0.001 |
| Meta-epidemiology (broad) | 0.009 | 0.005 |
| Bibliometrics | 0.014 | 0.014 |
| Science and technology studies | 0.001 | 0.001 |
| Scholarly communication | 0.003 | 0.002 |
| Open science | 0.002 | 0.002 |
| Research integrity | 0.002 | 0.001 |
| Insufficient payload (model declined to judge) | 0.007 | 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".