Evaluation of Fat Separation and Removal Methods to Prepare Low‐Fat Breast Milk for Fat‐Intolerant Neonates With Chylothorax
Bibliographic record
Abstract
BACKGROUND: The purpose of this study was to compare 2 methods (syringe and spoon methods) of removing the fat from the low-fat milk portion and compare 3 methods (refrigerated centrifuge, nonrefrigerated centrifuge, and refrigeration method) of separating breast milk into the fat and low-fat milk components. METHODS: Human milk was divided into 24 aliquots using the 3 separating methods, and 2 methods (syringe, spoon) were compared to extract the low-fat milk. Thirty-one human milk samples were separated into fatty and low-fat milk layers using 3 methods: 24-hour refrigerator storage (2°C), centrifuged at 3000 rpm for 15 minutes at room temperature, and spun in the refrigerated-centrifuge at 3000 rpm for 15 minutes at 2°C. After 24 hours of refrigeration, a syringe was used to remove the low-fat milk. Triglycerides were analyzed before and after separation and removal methods. RESULTS: For fat removal, the syringe method (1.2 g/dl, 95% confidence interval [CI], 1.1-1.4, fat content) left 34% less residual fat compared to the spoon method (1.9 g/dl, 95% CI, 1.5-2.3); this difference did not reach statistical significance (P = .065). For fat separation, the centrifuge methods (mean: 1.0 g/dl, 95% CI, 0.8-1.1) left significantly less residual fat than the refrigerator method (3.4 g/dl, 95% CI, 3.0-3.7; P < .0001). CONCLUSION: Using the syringe vs a spoon at removing the milk from the fat, although not statistically significant, was likely of clinical importance. A centrifuge was more effective at separating the fat in human 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 | 0.004 | 0.007 |
| Meta-epidemiology (narrow) | 0.001 | 0.000 |
| Meta-epidemiology (broad) | 0.001 | 0.000 |
| Bibliometrics | 0.001 | 0.000 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.000 | 0.000 |
| Open science | 0.001 | 0.000 |
| Research integrity | 0.000 | 0.000 |
| Insufficient payload (model declined to judge) | 0.001 | 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".