Bibliographic record
Abstract
BACKGROUND: Information on drug secretion into milk is insufficient due to the exclusion of lactating women from clinical trials and drug development processes. As a result, non-adherence to the necessary drug therapy and discontinuation of breastfeeding occur, even if the predicted level of infant exposure is low. In contrast, inadvertent infant exposure to drugs in breast milk continues to happen due to lack of rational risk assessment, resulting in serious toxicity cases including death. This problem is multifactorial, but one of the key elements is the lack of pharmacokinetic information on drug secretion into milk and resultant infant exposure levels, the first line of evidence for risk assessment. METHODS: Basic PK principles in drug excretion into milk were explained. The literature was scanned to identify approaches for PK data acquisition in this challenging field. RESULTS: This review describes the feasibility to develop such approaches, and the knowledge gaps that still exist. A combination of population pharmacokinetics approach (to estimate averages and variations of drug concentration profiles in milk) and physiologically-based pharmacokinetics modeling of infants (to predict the population profiles of infant drug exposure levels) appears useful. CONCLUSIONS: In order to facilitate participant enrollment and PK data acquisition in a timely manner, networks of investigators become crucial.
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How this classification was reachedexpand
Full frame distilled prediction
Teacher imitationNot calibrated prevalence, not ground truth. Human validation pending. Learned from the 10,348 direct Codex labels and 10,348 direct Gemma labels. Candidate is the union of thresholded teacher heads; consensus is their intersection. These outputs are machine_predicted_unvalidated and are not human labels or direct frontier model labels.
Codex and Gemma teacher scores by category
| Category | Codex | Gemma |
|---|---|---|
| Metaresearch | 0.003 | 0.000 |
| Meta-epidemiology (narrow) | 0.001 | 0.001 |
| Meta-epidemiology (broad) | 0.002 | 0.001 |
| Bibliometrics | 0.000 | 0.001 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.000 | 0.000 |
| Open science | 0.001 | 0.000 |
| Research integrity | 0.000 | 0.003 |
| Insufficient payload (model declined to judge) | 0.000 | 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 teacher head, 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".