Medications for patients who are lactating and breastfeeding: a decision tree.
Bibliographic record
Abstract
The authors present and describe a decision tree to provide guidance to clinicians who are administering medications to or prescribing for lactating (i.e., producing milk) and breastfeeding patients. It also applies to any patients who are expressing or pumping their milk to be used for feeding immediately or to be stored for future use or donation. Breastfeeding initiation rates and the duration of breastfeeding have increased dramatically in Canada over the past decades. As a result, lactating and breastfeeding patients are increasingly seen in many nonobstetric areas of health care, including emergency departments, radiology suites, and surgical departments. The decision tree has been designed for decision-making about any medication, including diagnostic agents, hormones, vaccines, herbs, over-the-counter products, and chemotherapeutic agents. The decision tree prompts clinicians to consider whether a medication can be deferred or, if it is required, how to make decisions about compatible or contraindicated medications. Given the benefits of breastfeeding to patients and their babies and children, clinicians should ensure that they promote, support, and protect breastfeeding when administering and prescribing 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 | 0.007 | 0.031 |
| Meta-epidemiology (narrow) | 0.001 | 0.001 |
| Meta-epidemiology (broad) | 0.001 | 0.002 |
| Bibliometrics | 0.003 | 0.002 |
| Science and technology studies | 0.001 | 0.000 |
| Scholarly communication | 0.003 | 0.003 |
| Open science | 0.001 | 0.001 |
| Research integrity | 0.001 | 0.003 |
| 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".