Evaluation of Fat Separation and Removal Methods to Prepare Low‐Fat Breast Milk for Fat‐Intolerant Neonates With Chylothorax
Why this work is in the frame
A frame that forgets how it found something cannot be audited. These are the routes that admitted this work.
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.
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.
Full frame distilled prediction
Teacher imitationNot 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.
Codex and Gemma teacher scores by category
| Category | Codex | Gemma |
|---|---|---|
| Metaresearch | 0.003 | 0.004 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.000 | 0.000 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.000 | 0.000 |
| Open science | 0.000 | 0.000 |
| Research integrity | 0.000 | 0.000 |
| Insufficient payload (model declined to judge) | 0.000 | 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 it