Development of home‐based methods to defat human milk for infants with chylothorax: An experimental study
Bibliographic record
Abstract
BACKGROUND: Chylothorax is a postoperative complication for infants with congenital heart defects; with high nutrition risk. Defatted human milk is recommended; however, refrigerated centrifugation to process milk poses accessibility barriers for many hospitals and families at home. Creation of a simplified home-based defatted milk protocol allows infants with chylothorax to be provided the immunological benefits of human milk postoperatively. METHODS: Milk from 20 mothers was tested to compare refrigerated centrifugation as the standard defatting technique against gravity-based methods: syringe tip-down and gravy separator. Two timeframes, 24 h and 48 h, were tested to determine if additional time had a significant impact on fat reduction. The MIRIS human milk analyzer provided results for fat, true protein, carbohydrate, and energy content. One-way analysis of variance was used to determine a significant difference on fat content among methods. RESULTS: All methods had a significant reduction in fat content, with centrifugation having the largest mean decline from 3.4 to 0.5 g/100 ml (P < 0.0001). The second most effective method to defat milk was 48-h gravy separator with a mean decline to 0.7 g/100 ml (P < 0.0001). Postpartum age of milk impacted the degree of fat removal in all methods. True protein content remained the same as baseline in all methods. CONCLUSION: A simplified home-based gravity separation method over 48 h reduced human milk fat by 80%. This is the first protocol to defat human milk without use of the more resource-intensive centrifugation method, that shows significant fat reduction with easy-to-use and accessible equipment for management of infants with chylothorax.
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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 | 0.001 |
| Meta-epidemiology (narrow) | 0.001 | 0.000 |
| Meta-epidemiology (broad) | 0.001 | 0.001 |
| Bibliometrics | 0.001 | 0.000 |
| Science and technology studies | 0.000 | 0.001 |
| Scholarly communication | 0.000 | 0.000 |
| Open science | 0.001 | 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".