Supporting Lactation in Otolaryngology Patients Through Medication Optimization, Radiology Considerations, and More
Bibliographic record
Abstract
Importance: The benefits of breastfeeding are well established, with the American Academy of Pediatrics and Canadian guidelines recommending exclusive breastfeeding for the first 6 months of life. However, maternal hospitalization, illness, medication use, and poor support can result in early termination of breastfeeding. Caring for breastfeeding patients in otolaryngology is a challenge because of the lack of literature regarding otolaryngology-specific medication safety, patient concerns, and inadequate education among otolaryngologists. This review highlights recent literature regarding lactation in otolaryngology patients, including medication, radiologic imaging, perioperative considerations, and subspecialty-specific considerations for lactating patients. Observations: The majority of common medications used in general otolaryngology are safe for breastfeeding patients, including antihistamines, mucolytics, antitussives, antifungals, and decongestants. Certain analgesics and anti-inflammatories, such as tramadol, are not preferred in breastfeeding individuals. Some subspeciality-specific medications such as biologics (dupilumab) and methotrexate should be avoided. Lactating patients require special perioperative attention to ensure that optimal patient care is provided, such as managing supply, considering length of surgery, managing postoperative pain, and determining the safe amount of time until an infant can be fed. Conclusions and Relevance: Most medications can be safely used with lactating patients. If physicians are unsure about a medication's safety, they should consult appropriate resources prior to recommending breastfeeding cessation or to discard pumped milk.
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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 | 0.007 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.001 | 0.001 |
| Bibliometrics | 0.000 | 0.000 |
| Science and technology studies | 0.001 | 0.001 |
| Scholarly communication | 0.001 | 0.002 |
| Open science | 0.000 | 0.001 |
| Research integrity | 0.001 | 0.002 |
| 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".