Melatonin use during pregnancy and lactation: A scoping review of human studies
Bibliographic record
Abstract
OBJECTIVE: The prevalence of sleep disorders during the perinatal period is high and large health administrative database surveys have shown that the use of exogenous melatonin in pregnant populations is quite common, about 4%. Much of the concern about using melatonin during pregnancy and breastfeeding stems from animal research. Thus, the objective of this article is to provide a critical review of human studies related to exogenous melatonin use during pregnancy and breastfeeding. METHODS: The electronic databases Ovid, MEDLINE, Embase, and the Cochrane Library were searched using terms and keywords related to melatonin, pregnancy, and breastfeeding. RESULTS: Fifteen studies were included in this review. Eight focused on melatonin use during pregnancy and seven focused on melatonin use during breastfeeding. There was a variety of study designs, including case reports, cohort studies, and clinical trials. There is a lack of randomized, controlled trials examining the efficacy and safety of melatonin as a treatment for sleep disorders during pregnancy or breastfeeding and, notably, insomnia was not the primary outcome measure in any of the studies included in this review. Clinical trials that used exogenous melatonin during pregnancy and breastfeeding for other clinical conditions have not suggested major safety concerns or adverse events. CONCLUSION: Contrary to what animal studies have suggested, evidence from clinical studies to date suggests that melatonin use during pregnancy and breastfeeding is probably safe in humans. This review further emphasizes the need for clinical studies on sleep disorders, including exogenous melatonin, during pregnancy and lactation.
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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 | 0.005 | 0.019 |
| Meta-epidemiology (narrow) | 0.001 | 0.001 |
| Meta-epidemiology (broad) | 0.005 | 0.004 |
| Bibliometrics | 0.010 | 0.009 |
| Science and technology studies | 0.001 | 0.001 |
| Scholarly communication | 0.002 | 0.002 |
| Open science | 0.001 | 0.001 |
| Research integrity | 0.002 | 0.001 |
| 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".