Cow’s Milk Contamination of Human Milk Purchased via the Internet
Bibliographic record
Abstract
BACKGROUND: The US Food and Drug Administration recommends against feeding infants human milk from unscreened donors, but sharing milk via the Internet is growing in popularity. Recipient infants risk the possibility of consuming contaminated or adulterated milk. Our objective was to test milk advertised for sale online as human milk to verify its human origin and to rule out contamination with cow's milk. METHODS: We anonymously purchased 102 samples advertised as human milk online. DNA was extracted from 200 μL of each sample. The presence of human or bovine mitochondrial DNA was assessed with a species-specific real-time polymerase chain reaction assay targeting the nicotinamide adenine dinucleotide (NADH) dehydrogenase subunit 5 gene. Four laboratory-created mixtures representing various dilutions of human milk with fluid cow's milk or reconstituted infant formula were compared with the Internet samples to semiquantitate the extent of contamination with cow's milk. RESULTS: All Internet samples amplified human DNA. After 2 rounds of testing, 11 samples also contained bovine DNA. Ten of these samples had a level of bovine DNA consistent with human milk mixed with at least 10% fluid cow's milk. CONCLUSIONS: Ten Internet samples had bovine DNA concentrations high enough to rule out minor contamination, suggesting a cow's milk product was added. Cow's milk can be problematic for infants with allergy or intolerance. Because buyers cannot verify the composition of milk they purchase, all should be aware that it might be adulterated with cow's milk. Pediatricians should be aware of the online market for human milk and the potential risks.
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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 | 0.002 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.001 | 0.001 |
| Science and technology studies | 0.000 | 0.001 |
| Scholarly communication | 0.001 | 0.000 |
| Open science | 0.000 | 0.000 |
| Research integrity | 0.001 | 0.000 |
| 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".