Growth and Body Composition of Human Milk‐Fed Premature Infants Provided with Extra Energy and Nutrients Early after Hospital Discharge: One Year Follow‐up
Bibliographic record
Abstract
The purpose of this pilot was to investigate the impact of early post‐hospital discharge supplementation of human milk with a multi‐nutrient fortifier on the growth and body composition of premature infants to one year corrected age (CA). Predominantly human milk‐fed infants (750–1800g birth weight) were randomized to an intervention (n=19) or control (n=20) group at discharge. Infants in the intervention received ~½ of their feedings supplemented with a multi‐nutrient fortifier (24kcal/fl oz, 22g protein/L plus other nutrients) for 12 weeks. Anthropometrics were determined at discharge and at 4, 6 and 12 months CA, and body composition at 4 and 12 months CA using dual energy x‐ray absorptiometry. Intervention infants were heavier, and longer at 12 months CA compared to control infants ( p <0.005). Intervention infants born ≤ 1250 g had a larger mean head circumference throughout the first year of life ( p =0.0002). Whole body bone mineral content at 4 and 12 months CA was greater in the intervention ( p =0.02) but not when controlled for length. In conclusion, adding a multi‐nutrient fortifier to the milk provided to predominantly human milk‐fed premature infants early after discharge results in sustained differences weight, length, and in smaller babies, head circumference for the first year of life. Funded by the Canadian Institute of Health Research(CIHR) and the CIHR Training Grant in Clinical Research
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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) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.001 | 0.001 |
| 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 | 0.001 |
| 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".