State of the evidence from clinical trials on human milk fortification for preterm infants*
Bibliographic record
Abstract
Infants born preterm or low birth weight are at risk for morbidity, mortality and later neuroimpairment. Appropriate early post-natal growth is associated with better outcomes in-hospital and post-discharge. Therefore, nutritional strategies that support growth may improve the long-term health of this population. Mother's milk with donor milk as a supplement are preferred sources of nutrition for these infants but may not always support growth, especially amongst infants born of very low birth weight (<1500 g) and or those with a major morbidity. Systematic reviews of randomised controlled trials to date demonstrate that multi-nutrient fortification of human milk improves in-hospital growth of preterm infants although data on long-term neurodevelopment are lacking. Further, individualised approaches to fortification based on milk analysis or the infant's metabolic response may improve growth over standard fortification. The evidence is insufficient to inform the timing of introducing fortifier, routine fortification of feeds post-discharge or routine use of fortifiers made from human instead of bovine milk. Importantly, there is insufficient data to determine if these fortification practices improve relevant clinical or neurodevelopmental outcomes. In sum, there is an urgent need for well-designed clinical trials to assess potential benefits and risks of fortification practices and at what cost.
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.
How this classification was reachedexpand
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 | 0.006 | 0.008 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.002 | 0.002 |
| Bibliometrics | 0.000 | 0.000 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.000 | 0.000 |
| Open science | 0.001 | 0.000 |
| Research integrity | 0.000 | 0.001 |
| 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 itClassification
machine, unvalidatedMachine predicted; a candidate call from one teacher head, 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".