Infant drug exposure via breast milk
Bibliographic record
Abstract
More than half of women take medications during breastfeeding, predisposing their infants to medication exposure via breast milk. As a result, adverse drug reactions may emerge in the infant, although they are rarely reported. Disposition of maternal drugs in breast milk is described with several key parameters, which include relative infant dose (RID): infant drug intake via milk (weight- and time-adjusted) expressed as a percentage of the similarly adjusted mother's dose. Most drugs show RID values of <10%, indicating that drug concentrations in infant serum do not reach a level known to be therapeutic in adults unless drug clearance is markedly lower than the adult level on a weight basis. RID is a function of milk-to-(maternal) plasma drug concentration ratio (MP ratio) and maternal drug clearance. Therefore, MP ratio between drugs must be interpreted not by itself but with maternal drug clearance of each drug. This is why some drugs such as phenobarbital show an MP ratio of <1 but an RID as high as 50-70%, while morphine shows an MP ratio of 2 but an RID in the range of 5%. Using RID, we interpreted case reports of infant adverse outcomes, and we observed cases with relatively low infant serum concentrations of drug, consistent with low RID, as well as those with near- or above-adult therapeutic serum concentrations, with or without increased drug intake (i.e. high RID). It is important to consider both pharmacokinetic and pharmacodynamic factors in interpreting adverse outcomes in infants breastfed by a mother taking medications.
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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 | 0.001 | 0.003 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.001 | 0.000 |
| Bibliometrics | 0.001 | 0.001 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.001 | 0.001 |
| Open science | 0.000 | 0.001 |
| Research integrity | 0.001 | 0.001 |
| Insufficient payload (model declined to judge) | 0.008 | 0.002 |
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".