The impact of storage, handling, and treatment on nutritional quality and safety of animal milk: A protocol for the systematic review and meta‐analysis
Bibliographic record
Abstract
Objectives: Human milk has been shown to reduce severe morbidity in preterm/low-birth-weight infants and is therefore the recommended nutritional source. When infants cannot receive maternal milk, donor human milk (DHM) is recommended. The use of human milk banking facilities is increasing to meet the need for DHM. DHM is unique compared to maternal milk as it must be processed and stored. The processing and storage of animal milk has been more rigorously studied than human milk and can serve as proxy to create DHM banking guidelines. Methods and Analysis: We will search electronic databases, grey literature, and the websites of relevant international organizations. We will include studies that evaluated the impact of storage, handling, and treatment on the nutritional quality and safety of animal milk. We will not restrict study date, language, or design. If sufficient homogeneity exists between studies, we will conduct a meta-analysis. We will evaluate the methodological quality of each study using the SYRCLE's (Systematic Review Centre for Laboratory Animal Experimentation) risk of bias tool. (1) We will evaluate the overall certainty of the evidence using the Grading of Recommendations Assessment, Development, and Evaluation approach. Conclusion: In this systematic review and meta-analysis, commissioned by the World Health Organization, we will synthesize the available literature regarding the impact of various storage, handling, and treatment practices on the nutritional quality and safety of animal milk.
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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 | 0.098 | 0.148 |
| Meta-epidemiology (narrow) | 0.006 | 0.005 |
| Meta-epidemiology (broad) | 0.020 | 0.029 |
| Bibliometrics | 0.012 | 0.012 |
| Science and technology studies | 0.003 | 0.005 |
| Scholarly communication | 0.007 | 0.006 |
| Open science | 0.005 | 0.004 |
| Research integrity | 0.009 | 0.007 |
| Insufficient payload (model declined to judge) | 0.054 | 0.007 |
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".