Comparing Impacts of Donor Human Milk to Formula Supplementation on the Gut Microbiome of Full-Term Infants Born Via Cesarean Section: Protocol for a Pilot Randomized Controlled Trial
Bibliographic record
Abstract
BACKGROUND: A disrupted gut microbiome during an infant's first 1000 days of life can lead to long-lasting negative effects on child health. Cesarean delivery and formula feeding are two factors that can detrimentally impact infant microbiome development as well as maternal mental health. Donor human milk may be a superior supplementation alternative to formula. RESEARCH AIM: To examine donor human milk supplementation compared to formula supplementation in full-term infants born via Cesarean section and the impact on the infant gut microbiome, infant health outcomes, breastfeeding outcomes, and maternal mental health.Methods and Planned Analyses:We are conducting a pilot clinical randomized controlled trial, comparing donor human milk to formula supplementation for 187 full-term infants born via Cesarean section who are breastfeeding and require supplementation in the first postnatal week of life. Infant stool samples, breastfeeding outcomes, maternal mental health, and child health outcomes will be measured at 1-week, 3-, 6-, and 12-months postpartum. Additionally, child health and maternal mental health are being assessed at 18- and 36-months postpartum. DISCUSSION: This study will generate essential data on the association between supplementation types and the full-term infant microbiome, breastfeeding exclusivity and duration, and infant health. It will also provide preliminary data to inform a multi-site, longitudinal mixed-methods randomized controlled trial that will assess longer term child health outcomes. This evidence may be used to inform guidelines and policies that will increase accessibility to and raise awareness of donor human milk as a supplementation option in this population.
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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 | 0.002 | 0.000 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.001 | 0.000 |
| Bibliometrics | 0.001 | 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) | 0.000 | 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".