Effects of Domperidone in Increasing Milk Production in Mothers with Insufficient Lactation for Infants in the Neonatal Intensive Care Unit
Bibliographic record
Abstract
Breast milk is the optimum for all infants, but hospitalization in the neonatal intensive care unit can cause separation of mothers and infants, which often interferes with milk secretion. Some reports show that domperidone is effective in promoting milk secretion. However, the Food and Drug Administration in the United States cautioned to not use domperidone for increasing milk volume because domperidone carries some risk of cardiac events, including QT prolongation, cardiac arrest, and sudden death. In contrast, it is used in Canada, Australia, and the United Kingdom with safety. The pharmacodynamics and pharmacokinetics of drugs may vary by race or ethnic origin, and it is not known whether domperidone is effective or safe for Japanese. In this study we report the effects of domperidone for Japanese mothers with insufficient lactation. Ten mothers were enrolled in a pilot study. After confirming that there were no abnormal findings on the electrocardiogram, the mothers were administered domperidone. Seven of 10 who took domperidone increased their milking volume. Prolactin was increased in 9 of 10 mothers. Adverse events were observed in two mothers, one headache and one abdominal pain; all symptoms were mild and improved promptly; and there were no adverse cardiac events. These results are consistent with reports from other countries. Domperidone may tentatively be considered effective for increasing milk secretion in Japanese mothers as in other populations. Our preliminary study of 10 cases indicates the need for further studies with larger sample sizes to assess the efficacy and safety of domperidone.
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 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 | 0.001 | 0.001 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.001 | 0.001 |
| 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".