O-122 Target Fortification (tfo) Of Breast Milk With Fat, Protein And Carbohydrates For Preterm Infants: Influence Of Commercially Available Fortifiers On Macronutrient Intake
Bibliographic record
Abstract
Background TFO of breast milk (BM) overcomes the uncertainty of macronutrient intake in fixed dose fortification (FDF) resulting from natural variation in breast milk and improves growth of VLBW infants. Objective To study variation of macronutrient intake and energy ratios when TFO is done using four different milk fortifiers and based on various frequencies of milk measurements. Study design Ten infants, GA: 26.1 ± 1.3 wks, BW: 890 ± 210 g. Daily measurements (n = 210) of native BM’s fat, protein (NIR milk analyzer), and lactose (UPLC-MS/MS) levels provided the basis for model calculation to add fat, protein and carbohydrates using modular products after FDF with either FM85, FMS, Enfamil, or Similac to meet ESPGHAN recommendations. BM measurement frequencies were 7/wk, 5/wk, 3/wk, 2/wk, 1/wk, and 0/wk (only FDF). Results Measurement 2/wk increased mean macronutrient intake and day-to-day variation was not higher compared to native BM. Day-to-day variation decreased with increasing frequency of milk analysis (Fig). After adding FDF mean carbohydrate level already exceeded in 3 fortifiers and median fat level in 1 fortifier leading to higher calorie intake than target levels. TFO 7/wk achieved macronutrient levels close to target when routine fortifier was composed with 1.1 g protein/dL (no fat, no CHO) whereas for measurements 1–2/wk, it was achieved with composition of fat 0.4g/dL, protein 1.2g/dL and carbohydrate 1.1g/dL. Conclusions Measurements of macronutrient 1–2/wks might provide a reasonable balance between workload and clinical outcome. Due to different composition of fortifiers, either target values for macronutrients or composition of fortifier for use with TFO needs to be reconsidered in order to achieve recommended intake.
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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".