Is Breast Best? Examining the effects of alcohol and cannabis use during lactation
Bibliographic record
Abstract
Maternal drug use during lactation may have adverse effects on the health of their children. Two common drugs used during this period are alcohol and cannabis. A literature search was conducted using PubMed, CINAHL, Nursing and Allied Health, and Google Scholar with the following search terms: marijuana, cannabis, THC, alcohol, ethanol, breastfeeding, lactation, and breastmilk. The search strategy was restricted to papers since the year 2000, and limited to English language journals. Reference lists were also used to capture any articles that were missed from the database searches. In total, 19 articles were found related to alcohol and breastfeeding (n = 17 original research papers; n = 2 systematic reviews), and 4 articles were specific to cannabis (n = 2 original papers; n = 2 systematic reviews). The most common outcomes associated with alcohol consumption and breastfeeding included changes in sleep patterns, reduced milk production and flow, lower milk intake, and impaired immune function. Maternal outcomes related to cannabis consumption included panic attacks, delayed response time, increased heart rate, reduced short-term memory, dizziness, and impaired motor performance; infant outcomes associated with maternal cannabis use and breastfeeding were reduced muscular tonus, poor sucking, and growth delay and restriction. Mothers should be advised to refrain from substance use during the lactation period for the health and safety of their children.
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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 | 0.002 | 0.011 |
| Meta-epidemiology (narrow) | 0.001 | 0.000 |
| Meta-epidemiology (broad) | 0.003 | 0.002 |
| Bibliometrics | 0.004 | 0.004 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.002 | 0.002 |
| Open science | 0.001 | 0.001 |
| Research integrity | 0.001 | 0.001 |
| Insufficient payload (model declined to judge) | 0.003 | 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".