Impact of Neonatal Intensive Care Unit Admission on Bacterial Colonization of Donated Human Milk
Bibliographic record
Abstract
BACKGROUND: Unpasteurized human donor milk typically contains a variety of bacteria. The impact of neonatal intensive care unit (NICU) admission of the donor's infant and duration of lactation on bacterial contamination of human milk is unknown. Research aim: This study aimed (a) to describe the frequency/concentration of skin commensal bacteria and pathogens in unpasteurized human donor milk and (b) to assess the impact of NICU admission and (c) the duration of milk expression on bacterial colonization of donated milk. METHODS: The authors conducted a retrospective cohort study of human milk donated to the Rogers Hixon Ontario Human Milk Bank from January 2013 to June 2014. Milk samples from each donor were cultured every 2 weeks. RESULTS: cfu/L, a local threshold for allowable bacteria prior to pasteurization. The mean (standard deviation) donation period per donor was 13.0 (7.5) weeks. Milk from mothers with NICU exposure had significantly higher concentrations of commensals, but not pathogens, at every time period compared with other mothers. For every 1-month increase in donation from all donors, the odds ratio of presence of any commensal in milk increased by 1.13 (95% confidence interval [1.03, 1.23]) and any pathogen by 1.31 (95% confidence interval [1.20, 1.43]). CONCLUSION: Commensal bacteria were more abundant in donor milk expressed from mothers exposed to neonatal intensive care. Bacterial contamination increased over the milk donation period.
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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 | 0.005 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.000 | 0.001 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.001 | 0.000 |
| Open science | 0.000 | 0.001 |
| Research integrity | 0.000 | 0.001 |
| 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".