Marijuana and Breastfeeding: Applicability of the Current Literature to Clinical Practice
Bibliographic record
Abstract
With recent legalization of marijuana in numerous U.S. states, the risk of marijuana exposure via breast milk is a rising concern. This review analyzes the available human and animal literature regarding maternal use of marijuana during lactation. The findings can be categorized into four areas of analysis: effects of marijuana on the mother, transfer into milk, transfer to the offspring, and effects on the offspring. Human and animal data have reported decreased prolactin levels as well as potential maternal psychological changes. Animal and human studies have reported transfer into milk; levels were detected in animal offspring, and metabolites were excreted by both human and animal offspring. Further, animal data have predominately displayed motor, neurobehavioral, and developmental effects, whereas human data suggested possible psychomotor outcomes; however, some studies reported no effect. Despite these results, many human studies were marred by limitations, including small sample sizes and confounding variables. Also, the applicability of animal data to the human population is questionable and the true risk of adverse effects is not entirely known. There are large gaps in the literature that need to be addressed; in particular, studies need to focus on evaluating the short- and long-term consequences of maternal marijuana use for the infant and the potential for different risks based on the frequency of maternal use. Until further evidence becomes available, practitioners need to weigh the benefits of breastfeeding for mother and child, with the potential influence of marijuana on infant development when determining the infant's most suitable form of nutrition.
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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 | 0.095 |
| Meta-epidemiology (narrow) | 0.001 | 0.001 |
| Meta-epidemiology (broad) | 0.002 | 0.001 |
| Bibliometrics | 0.007 | 0.009 |
| Science and technology studies | 0.001 | 0.003 |
| Scholarly communication | 0.005 | 0.006 |
| Open science | 0.002 | 0.003 |
| Research integrity | 0.003 | 0.004 |
| Insufficient payload (model declined to judge) | 0.005 | 0.001 |
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".