Nutritional interventions to prevent retinopathy of prematurity
Bibliographic record
Abstract
Very preterm infants are at high risk of growth failure. Poor weight gain is a prominent risk factor for retinopathy of prematurity (ROP) and optimizing nutrition could potentially promote growth and reduce ROP. Most infants at risk of ROP need parenteral nutrition initially and studies of enhanced parenteral provision of lipids and amino acids have suggested a beneficial effect on ROP. Higher amino acid intake was associated with lower incidence of hyperglycemia, a risk factor for ROP. For very preterm infants, providing unpasteurized fortified raw maternal breast milk appears to have a dose-dependent preventive effect on ROP. These infants become deficient in arachidonic acid (ArA) and docosahexaenoic acid (DHA) after birth when the maternal supply is lost. Earlier studies have investigated the impact of omega-3 fatty acids on ROP with mixed results. In a recent study, early enteral supplementation of ArA 100 mg/kg/d and DHA 50 mg/kg/d until term equivalent age reduced the incidence of severe ROP by 50%. IMPACT: Previous reviews of nutritional interventions to prevent morbidities in preterm infants have mainly addressed bronchopulmonary dysplasia, brain lesions and neurodevelopmental outcome. This review focusses on ROP. Neonatal enteral supplementation with arachidonic acid and docosahexaenoic acid, at levels similar to the fetal accretion rate, has been found to reduce severe ROP by 50% in randomized controlled trials.
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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 | 0.002 |
| Meta-epidemiology (narrow) | 0.001 | 0.000 |
| Meta-epidemiology (broad) | 0.002 | 0.001 |
| Bibliometrics | 0.002 | 0.002 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.001 | 0.001 |
| Open science | 0.001 | 0.001 |
| Research integrity | 0.001 | 0.001 |
| Insufficient payload (model declined to judge) | 0.005 | 0.001 |
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".