Donated breast milk stored in banks versus breast milk purchased online.
Bibliographic record
Abstract
QUESTION: One of my patients asked if she could buy human milk on the Internet to feed her infant if the need arose. Is using donated breast milk from the milk bank safer than buying it online? ANSWER: The World Health Organization and the American Academy of Pediatrics recommend the use of donated breast milk as the first alternative when maternal milk is not available, but the Canadian Paediatric Society does not endorse the sharing of unprocessed human milk. Human breast milk stored in milk banks differs from donor breast milk available via the Internet owing to its rigorous donor-selection process, frequent quality assurance inspections, regulated transport process, and pasteurization in accordance with food preparation guidelines set out by the Canadian Food Inspection Agency. Most samples purchased online contain Gram-negative bacteria or have a total aerobic bacteria count of more than 10(4) colony-forming units per millilitre; they also exhibit higher mean total aerobic bacteria counts, total Gram-negative bacteria counts, coliform bacteria counts, and Staphylococcus spp counts than milk bank samples do. Growth of most bacteria species is associated with the number of days in transit, which suggests poor collection, storage, or shipping practices for milk purchased online.
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 | 0.003 | 0.025 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.001 | 0.001 |
| Bibliometrics | 0.001 | 0.001 |
| Science and technology studies | 0.001 | 0.001 |
| Scholarly communication | 0.001 | 0.003 |
| Open science | 0.001 | 0.001 |
| Research integrity | 0.002 | 0.002 |
| Insufficient payload (model declined to judge) | 0.039 | 0.004 |
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".