Natural Health Products (NHPs) in Pregnancy and Lactation: A Review of the Landscape and Blueprint for Change
Bibliographic record
Abstract
Introduction: Based on the perceived risk to newborns and pregnancy outcomes associated with certain drugs, women may be hesitant to prescribe and take drugs during pregnancy. In cases like these, pregnant women may seek treatment using natural health products (NHPs) as alternatives to drugs. Unfortunately, evidence of safety in pregnancy and lactation is unknown for many NHPs.\nObjectives: To review the present state of evidence on the safety of NHPs during pregnancy and lactation. To create a new system to validate evidence on NHPs during pregnancy and lactation designed to affect medical decision.\nMethodology: NHPs were systematically reviewed and in some cases, meta-analyzed for evidence of safety during pregnancy and lactation.\nResults: In total, 79 NHPs were systematically reviewed and 2 NHPs were meta-analyzed in order to determine the evidence of safety in pregnancy and lactation. Despite the presence of data (72/79 NHPs in pregnancy and 53/77 NHPs in lactation), the quality of the data was generally poor. Using evidence-based medicine principles, a new system of evaluating evidence was established for studies involving NHPs in pregnancy and lactation. A number of NHPs were identified as being of potential risk in pregnancy. A number of NHPs were identified as potentially being apparently safe in pregnancy and lactation. Blue cohosh is of potential concern for harm in pregnancy given an apparent dose-dependant relationship. \nConclusion: There is a large knowledge gap on the safety of NHPs in pregnancy, even more so in lactation. The new system for evaluating NHP safety in pregnancy and lactation will require validation. In order to improve the knowledge gap, future studies are proposed on NHPs in pregnancy and lactation as part of the newly formed MotherNature research network.
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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 | 0.016 | 0.035 |
| Meta-epidemiology (narrow) | 0.001 | 0.001 |
| Meta-epidemiology (broad) | 0.003 | 0.004 |
| Bibliometrics | 0.008 | 0.010 |
| Science and technology studies | 0.001 | 0.002 |
| Scholarly communication | 0.003 | 0.004 |
| Open science | 0.002 | 0.001 |
| Research integrity | 0.003 | 0.003 |
| Insufficient payload (model declined to judge) | 0.004 | 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".