Enhancing Human Milk Production With Domperidone in Mothers of Preterm Infants
Bibliographic record
Abstract
BACKGROUND: Mothers of preterm infants often are at risk of expressing an inadequate amount of milk for their infants and the use of galactogogues is often considered. Domperidone is a widely used galactogogue with little information available to guide clinicians regarding initiation, timing, and duration of treatment. Research aim: The primary objective of this study was to determine whether administration of domperidone within the first 21 days after delivery would lead to a higher proportion of mothers achieving a 50% increase in the volume of milk at the end of 14 days of treatment compared with mothers receiving placebo. METHODS: Eligible mothers were randomized to one of two treatment arms: Group A-domperidone 10 mg orally three times daily for 28 days; or Group B-placebo 10 mg orally three times daily for 14 days followed by domperidone 10 mg orally three times daily for 14 days. RESULTS: A total of 90 mothers of infants ≤ 29 weeks gestation were randomized. Mean milk volumes at entry were similar for both groups. More mothers achieved a 50% increase in milk volume after 14 days in Group A (77.8%) compared with Group B (57.8%), odds ratio = 2.56, 95% confidence interval [1.02, 6.25], p = .04. CONCLUSION: A greater number of mothers experienced a 50% or more increase in human milk volume, but the absolute increase in milk volume was modest.
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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 | 0.003 |
| 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) | 0.001 | 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 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".