Mood stabilizers during breastfeeding: a systematic review of the recent literature
Bibliographic record
Abstract
OBJECTIVE: This review examined the safety of mood stabilizers in exposed breastfed infants. METHODS: PubMed was searched for English language reports between 1 January 1995 and 30 August 2015 by using combinations of key words breastfeeding, lactation, postpartum period, puerperium, mood stabilizers, lithium, lamotrigine, valproate, carbamazepine, and oxcarbazepine. Case reports, case series, and prospective or cross-sectional studies including relevant data such as relative infant dose, milk-to-plasma ratio, infant drug plasma levels, and adverse events were identified. RESULTS: A total of 26 of 604 relevant reports in PubMed were included in the study. These reports included lamotrigine (122 cases in 12 reports), lithium (26 cases in five reports), carbamazepine (64 cases in five reports), valproate (nine cases in three reports), and oxcarbazepine (two cases in two reports). Of 26 reports, one report included both carbamazepine and valproate. The reports suggest that a considerable amount of lithium and lamotrigine are excreted into breast milk. There is a paucity of data on valproate and oxcarbazepine; however, the infant/maternal ratio of serum drug concentration seems to be lower in valproate exposure compared to other mood stabilizers. The incidence of adverse events in infants exposed to mood stabilizers is reported to be very low. CONCLUSIONS: The current data suggest that mood stabilizers can be prescribed without any adverse events in most infants in lactating women. The available reports also suggest a low prevalence rate of laboratory abnormalities including hepatic, kidney, and thyroid functions in the infants. Additional studies examining short-term and especially long-term effects of mood stabilizers on breastfed infants are required.
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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 | 0.015 |
| Meta-epidemiology (narrow) | 0.001 | 0.001 |
| Meta-epidemiology (broad) | 0.005 | 0.005 |
| Bibliometrics | 0.011 | 0.011 |
| Science and technology studies | 0.001 | 0.001 |
| Scholarly communication | 0.002 | 0.002 |
| Open science | 0.001 | 0.001 |
| Research integrity | 0.001 | 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".