The Design and Mechanics of an Accessible Human Milk Research Biorepository
Bibliographic record
Abstract
Introduction: Human milk is the normative standard for infant/toddler nutrition. To better understand human milk's imprinting on health, and inform complex decisions about maternal medication, substance use, and other exposures during lactation, researchers at University of California San Diego (UC San Diego) established Mommy's Milk, a Human Milk Research Biorepository (HMB). Materials and Methods: The HMB was founded in 2014 with the goal of building a constant but rotating inventory of 3,000 human milk samples available for future research. Following informed consent, women in the United States or Canada provide 50 mL up to a full pump of expressed breast milk. Participants are also interviewed about their sociodemographic characteristics, pregnancy history, dietary intake, maternal stress, anxiety and depression, breastfeeding behaviors, and signs and symptoms of potential adverse reactions in the offspring. Data on growth of the infant/toddler are captured from medical records, and neurodevelopmental assessments are conducted longitudinally. Sample collections occur at UC San Diego, community sites, or the woman's home, and are aliquoted and stored at −80°C. Results: To date, 1,362 unique women have contributed to the HMB. The majority of mothers were between the ages of 31–35, and identified as White. The range of ages of breastfed offspring was well-represented through 23 months. Conclusions: The HMB is a well-characterized, accessible research resource that can contribute to better understanding of the characteristics of human milk, and potential effects of maternal medications, substances, and other environmental agents on the health and development of the breastfed infant/toddler.
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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 | 0.030 | 0.034 |
| Meta-epidemiology (narrow) | 0.002 | 0.002 |
| Meta-epidemiology (broad) | 0.001 | 0.001 |
| Bibliometrics | 0.002 | 0.001 |
| Science and technology studies | 0.003 | 0.002 |
| Scholarly communication | 0.003 | 0.002 |
| Open science | 0.006 | 0.006 |
| Research integrity | 0.002 | 0.002 |
| Insufficient payload (model declined to judge) | 0.040 | 0.013 |
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".