Bibliographic record
Abstract
Although most medications can be taken safely during breastfeeding, potential risks of infant toxicity do exist because all medications will be excreted into the breast milk to some extent. The amount of medication excreted in the milk depends mainly on (i) within-drug variation, such as dosing; (ii) between-drug variation including chemical characteristics of the medication; and (iii) host factors, such as maternal pharmacokinetics (PK), including variations of pregnancy-associated changes and their post-partum recovery. Neonatal drug exposure is usually assessed by calculating an expected total infant daily dose through breast milk and comparing it to the normal therapeutic dose. However, clinical PK studies in this population are challenging to conduct. Recently, research methods using population PK analyses and physiologically-based PK modeling and simulation techniques have been recognized as a complementary approach to the conventional PK studies in this field. These efforts are important for rational risk assessment balancing the toxicity risk against the benefits of human milk. Health benefits of lactation for both mother and child are significant and a decision to withhold from this should not be taken lightly. In case limited information is present, additional expertise from pharmacists or clinical pharmacologist with expertise in this area should be sought.
Fetched live from OpenAlex and de-inverted. Abstracts are not stored in this database: the inverted indexes are 8.6 GB of the frame’s 9.3 GB of text, and the host has 13 GB free.
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 | 0.000 | 0.001 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.001 | 0.001 |
| Bibliometrics | 0.001 | 0.001 |
| Science and technology studies | 0.000 | 0.001 |
| Scholarly communication | 0.001 | 0.001 |
| Open science | 0.001 | 0.001 |
| Research integrity | 0.001 | 0.002 |
| Insufficient payload (model declined to judge) | 0.005 | 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".