Lactation Induction in a Transgender Woman Wanting to Breastfeed: Case Report
Bibliographic record
Abstract
CONTEXT: Breastfeeding is known to have many health and wellness benefits to the mother and infant; however, breastfeeding in trans women has been greatly under-researched. OBJECTIVE: To review potential methods of lactation induction in trans women wishing to breastfeed and to review the embryological basis for breastfeeding in trans women. DESIGN: This article summarizes a case of successful lactation in a trans woman, in which milk production was achieved in just over 1 month. SETTING: This patient was followed in an outpatient endocrinology clinic. PARTICIPANT: A single trans woman was followed in our endocrinology clinic for a period of 9 months while she took hormone therapy to help with lactation. INTERVENTIONS: Readily available lactation induction protocols for nonpuerpural mothers were reviewed and used to guide hormone therapy selection. Daily dose of progesterone was increased from 100 mg to 200 mg daily. The galactogogue domperidone was started at 10 mg 3 times daily and titrated up to effect. She was encouraged to use an electric pump and to increase her frequency of pumping. MAIN OUTCOME MEASURE: Lactation induction. RESULTS: At one month, she had noticed a significant increase in her breast size and fullness. Her milk supply had increased rapidly, and she was producing up to 3 to 5 ounces of milk per day with manual expression alone. CONCLUSIONS: We report the second case in the medical literature to demonstrate successful breastfeeding in a trans woman through use of hormonal augmentation.
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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 | 0.000 | 0.002 |
| Meta-epidemiology (narrow) | 0.001 | 0.001 |
| Meta-epidemiology (broad) | 0.001 | 0.001 |
| Bibliometrics | 0.001 | 0.001 |
| Science and technology studies | 0.002 | 0.001 |
| Scholarly communication | 0.001 | 0.001 |
| Open science | 0.001 | 0.001 |
| Research integrity | 0.003 | 0.002 |
| Insufficient payload (model declined to judge) | 0.002 | 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 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".