Human Milk Expression After Domperidone Treatment in Postpartum Women: A Systematic Review and Meta-Analysis of Randomized Controlled Trials
Bibliographic record
Abstract
Background: Insufficient milk production is among the most cited reasons by mothers for discontinuing breastfeeding. Medications that can increase milk production, such as domperidone, an off-label galactagogue, are often prescribed. Domperidone is controversial as it is not approved for any purpose in the United States and is approved only for gastrokinetic purposes in Canada and other countries. Research aim: The aim was to update the existing literature on the efficacy of domperidone as a galactagogue compared to placebo when given to mothers with insufficient human milk production. The primary outcome is the change in expressed human milk volume per day from baseline. Methods: The authors independently searched the literature from inception to May 2018. The search included any randomized controlled trials examining the efficacy of domperidone increasing mothers’ expressed human milk, measured via a human milk pump. Both authors independently assessed quality and risk of bias and extracted relevant data. Meta-analysis on expressed human milk volume per day was performed. Results: Seven studies met the inclusion criteria for review; two were excluded from the meta-analysis due to quality grading and insufficient reporting of the outcome of interest. Five studies ( N = 239) were combined in the meta-analysis. The effect size showed an increase in the mean difference of expressed human milk volume in mothers given domperidone, 93.97 mL per day (95% CI [71.12, 116.83 mL]; random effect, T 2 0.00, I 2 0%). Conclusion: This meta-analysis reports a significant improvement in expressed human milk volume per day with the use of domperidone in mothers experiencing insufficient human milk production.
Fetched live from OpenAlex and de-inverted. Abstracts are not stored in this database: the inverted indexes are 8.6 GB of the frame’s 9.3 GB of text, and the host has 13 GB free.
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 | 0.015 | 0.037 |
| Meta-epidemiology (narrow) | 0.003 | 0.002 |
| Meta-epidemiology (broad) | 0.022 | 0.041 |
| Bibliometrics | 0.006 | 0.006 |
| Science and technology studies | 0.001 | 0.001 |
| Scholarly communication | 0.003 | 0.002 |
| Open science | 0.002 | 0.001 |
| Research integrity | 0.003 | 0.002 |
| Insufficient payload (model declined to judge) | 0.003 | 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".