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Record W2534201045 · doi:10.1089/bfm.2016.0139

Storage of Unfed and Leftover Pasteurized Human Milk

2016· article· en· W2534201045 on OpenAlexaff
Ting Meng, Maryanne T. Perrin, Jason A. Osborne, Frances Jones, April Fogleman

Bibliographic record

VenueBreastfeeding Medicine · 2016
Typearticle
Languageen
FieldNursing
TopicInfant Nutrition and Health
Canadian institutionsB.C. Women's Hospital & Health Centre
FundersNorth Carolina State University
KeywordsPasteurizationFood scienceMedicineChemistry

Abstract

fetched live from OpenAlex

OBJECTIVE: To determine the impact of storage on bacterial growth and immunological activity of pasteurized human milk and leftover pasteurized human milk that has been exposed to the microflora in an infant's mouth. MATERIALS AND METHODS: Eighteen mother-infant dyads participated in two separate studies. Mother's milk was pasteurized, and each baby was fed 1 to 2 ounces. Pasteurized and leftover pasteurized milk were stored at room (24°C) and refrigerated temperatures (4°C). After storage, milk was analyzed for bacteria, total protein, lysozyme activity, and secretory immunoglobulin A (SIgA) activity. RESULTS: In pasteurized and leftover pasteurized milk stored in the refrigerator for 7 days, total aerobic bacteria do not increase significantly and total protein and bioactive proteins are stable. At room temperature, there is a significant increase in total aerobic bacteria in leftover pasteurized milk during 12 hours of storage (p < 0.01) and a significant decrease in total protein and SIgA activity in pasteurized milk during 12 hours of storage (p = 0.02 and p = 0.03, respectively). CONCLUSIONS: When stored in the refrigerator, pasteurized and leftover pasteurized milk may be stored for at least 7 days when considering the variables studied. Caution should be used when storing pasteurized and leftover pasteurized milk at room temperature to prevent an increase in bacterial growth and a decrease in total protein and SIgA activity.

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 distilled prediction

Teacher imitation

Not 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.

metaresearch head score (Codex)0.000
metaresearch head score (Gemma)0.000
Version: codex-gemma-dda1882f352aValidation status: machine_predicted_unvalidated
Candidate categoriesnone
Consensus categoriesnone
DomainCandidate signal: none · Consensus signal: none
Study designCandidate signal: Bench or experimental · Consensus signal: none
GenreCandidate signal: Empirical · Consensus signal: Empirical
Teacher disagreement score0.871
Threshold uncertainty score0.499

Codex and Gemma teacher scores by category

CategoryCodexGemma
Metaresearch0.0000.000
Meta-epidemiology (narrow)0.0000.000
Meta-epidemiology (broad)0.0000.000
Bibliometrics0.0000.000
Science and technology studies0.0000.000
Scholarly communication0.0000.000
Open science0.0000.000
Research integrity0.0000.000
Insufficient payload (model declined to judge)0.0000.000

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.

Opus teacher head0.023
GPT teacher head0.296
Teacher spread0.273 · how far apart the two teachers sit on this one work
Validation statusscore_only:v0-immature-baseline · verbatim from the scoring run: score_only means the number may rank works, and no category label ships from it

Classification

machine, unvalidated

Machine predicted; a candidate call from one teacher head, not a consensus.

The models applied no category: nothing in the taxonomy fit this work.
Study designBench or experimental
Domainnot available
GenreEmpirical

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".

Quick stats

Citations17
Published2016
Admission routes1
Has abstractyes

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