Effect of Domperidone on the Composition of Preterm Human Breast Milk
Bibliographic record
Abstract
OBJECTIVE: Domperidone is increasingly prescribed to improve breast milk volume despite a lack of evidence regarding its effects on breast milk composition. We examined the effect of domperidone on the nutrient composition of breast milk. PATIENTS AND METHODS: Forty-six mothers who had delivered infants at <31 weeks' gestation, who experienced lactation failure, were randomly assigned to receive domperidone or placebo for 14 days. Protein, energy, fat, carbohydrate, sodium, calcium, and phosphate levels in breast milk were measured on days 0, 4, 7, and 14, serum prolactin levels were measured on days 0, 4, and 14, and total milk volume was recorded daily. Mean within-subject changes in nutrients and milk volumes were examined. RESULTS: Maternal and infant characteristics, serum prolactin level, and breast milk volume and composition were not significantly different between domperidone and placebo groups on day 0. By day 14, breast milk volumes increased by 267% in the domperidone-treated group and by 18.5% in the placebo group (P = .005). Serum prolactin increased by 97% in the domperidone group and by 17% in the placebo group (P = .07). Mean breast milk protein declined by 9.6% in the domperidone group and increased by 3.6% in the placebo group (P = .16). Changes in energy, fat, carbohydrate, sodium, and phosphate content were also not significantly different between groups. Significant increases were observed in breast milk carbohydrate (2.7% vs -2.7%; P = .05) and calcium (61.8% vs -4.4%; P = .001) in the domperidone versus placebo groups. No significant adverse events were observed among mothers or infants. CONCLUSION: Domperidone increases the volume of breast milk of preterm mothers experiencing lactation failure, without substantially altering the nutrient composition.
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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 | 0.000 | 0.000 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.000 | 0.000 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.000 | 0.000 |
| Open science | 0.000 | 0.000 |
| Research integrity | 0.000 | 0.000 |
| Insufficient payload (model declined to judge) | 0.000 | 0.000 |
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".