Microbial Contamination of Human Milk Purchased Via the Internet
Bibliographic record
Abstract
OBJECTIVE: To quantify microbial contamination of human milk purchased via the Internet as an indicator of disease risk to recipient infants. METHODS: Cross-sectional sample of human milk purchased via a popular US milk-sharing Web site (2012). Individuals advertising milk were contacted to arrange purchase, and milk was shipped to a rented mailbox in Ohio. The Internet milk samples (n = 101) were compared with unpasteurized samples of milk donated to a milk bank (n = 20). RESULTS: Most (74%) Internet milk samples were colonized with Gram-negative bacteria or had >10(4) colony-forming units/mL total aerobic count. They exhibited higher mean total aerobic, total Gram-negative, coliform, and Staphylococcus sp counts than milk bank samples. Growth of most species was positively associated with days in transit (total aerobic count [log10 colony-forming units/mL] β = 0.71 [95% confidence interval: 0.38-1.05]), and negatively associated with number of months since the milk was expressed (β = -0.36 [95% confidence interval: -0.55 to -0.16]), per simple linear regression. No samples were HIV type 1 RNA-positive; 21% of Internet samples were cytomegalovirus DNA-positive. CONCLUSIONS: Human milk purchased via the Internet exhibited high overall bacterial growth and frequent contamination with pathogenic bacteria, reflecting poor collection, storage, or shipping practices. Infants consuming this milk are at risk for negative outcomes, particularly if born preterm or are medically compromised. Increased use of lactation support services may begin to address the milk supply gap for women who want to feed their child human milk but cannot meet his or her needs.
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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 | 0.000 | 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) | 0.000 | 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".