Effect of domperidone to increase breast milk volume
Bibliographic record
Abstract
Mother's own breast milk is considered to be the optimal form of nutrition for all infants. Its use has been associated with a reduction in neonatal morbidities and improvements in neurodevelopmental outcome. Despite this compelling data, a significant discrepancy remains between the rates of breastfeeding initiation and the rates examining duration and exclusivity of breastfeeding. While the reasons for this decline are multifactorial, actual or perceived concerns regarding milk supply are common. Non-pharmacological measures considered to be the first choice for the treatment of low milk supply have been associated with a variable level of success in augmenting milk production. Thus, the use of a pharmacological agent is often prescribed. Many questions remain regarding the role and best option of galactogogues. Domperidone, a dopamine antagonist, is an effective treatment for most mothers with insufficient milk supply. Even at low doses, domperidone has been associated with increases in milk volumes in some women experiencing insufficient milk production. However, further study related to the optimal dose, duration of treatment, and safety consideration of both mothers and their infants is needed.
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 | 0.000 | 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) | 0.007 | 0.002 |
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".