Immunoglobulin A and Protein Content of Low‐Fat Human Milk Prepared for the Treatment of Chylothorax
Bibliographic record
Abstract
BACKGROUND: Several case studies report successful recovery from chylothorax while infants were fed low-fat human milk. The reported growth rates were inadequate despite milk supplementation with added medium-chain triglycerides (MCTs). The objective was to determine the effect that various human milk fat separating methods, refrigerated centrifuge, room temperature centrifuge, and refrigeration have on the loss of immunoglobulin A (IgA) and protein in the preparation of low-fat human milk. METHODS: Protein and IgA were measured in 31 samples of reduced-fat human milk. Reduced-fat breastmilk samples were prepared by separating the fat using 3 methods (refrigerated centrifuge, room temperature centrifuge, and a refrigeration method), followed by lower fat milk extraction by syringe. RESULTS: The refrigeration method decreased IgA concentration by 17% (P = .035) while centrifugation and fat removal from the human milk samples led to a 38% decline in IgA concentration in both the nonrefrigerated and refrigerated centrifuge samples (P < .0001 for both). Protein declined by 11% with refrigeration and fat removal (P < .0001) while centrifugation and fat removal decreased protein concentration by 31% (P < .0001) in both nonrefrigerated centrifuge and refrigerated centrifuge samples. CONCLUSIONS: Preparing low-fat human milk for patients with chylothorax decreased the IgA and protein contents. As well as fat (in the form of MCTs), protein likely needs to be supplemented for infants fed low-fat human milk to support adequate growth.
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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 | 0.000 | 0.001 |
| 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) | 0.001 | 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".