The Effect of Two Different Domperidone Doses on Maternal Milk Production
Bibliographic record
Abstract
BACKGROUND: The benefits of breast milk to the newborn infant are well established. The Canadian Paediatric Society recommends exclusive breastfeeding for the first 6 months of life for healthy, term infants. Mothers of premature newborns, however, may have difficulty providing an adequate supply of breast milk. Domperidone is officially used as a prokinetic agent. However, it is used widely around the world as a galactogogue. Despite its widespread use as a galactogogue, only a small number of investigators have studied domperidone for this indication. AIMS: The purpose of this study was to determine an optimal dosage of domperidone as a galactogogue. METHODS: Eligible subjects were randomized to receive domperidone 10 mg 3 times daily or domperidone 20 mg 3 times daily for 4 weeks. At week 5, the frequency was decreased to twice daily in both groups, and finally once daily for week 6. RESULTS: Over the entire first 4-week period, there was a significant increase in daily milk volumes within each group (P < .01). The between-group difference over this period, although not statistically significant, was clinically significant. Additionally, there was no significant within- or between-group difference during weeks 5 and 6. CONCLUSION: A dose of domperidone of 20 mg, 3 times daily instead of 10 mg, 3 times daily was associated with a clinical, but not statistically significant, increase in milk production.
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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 | 0.004 |
| Meta-epidemiology (narrow) | 0.001 | 0.000 |
| Meta-epidemiology (broad) | 0.001 | 0.001 |
| Bibliometrics | 0.000 | 0.000 |
| Science and technology studies | 0.000 | 0.001 |
| Scholarly communication | 0.001 | 0.000 |
| Open science | 0.001 | 0.000 |
| Research integrity | 0.001 | 0.001 |
| Insufficient payload (model declined to judge) | 0.002 | 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".