Infant Formula for the Prevention and Treatment of Neonatal Hypoglycaemia: A Systematic Review and Meta‐Analysis
Bibliographic record
Abstract
ABSTRACT Aim Neonatal hypoglycaemia is a common metabolic disorder in newborns; if severe or prolonged, it can lead to brain injury. Formula is sometimes used to treat neonatal hypoglycaemia. This study aims to synthesise evidence on the effectiveness of infant formula for the prevention and treatment of neonatal hypoglycaemia. Methods Randomised controlled trials (RCTs) and cohort studies comparing infants given formula to those who were not were included. Four databases and four trial registries were searched. Study quality was evaluated using the Cochrane Risk of Bias‐1 tool for RCTs and the Newcastle‐Ottawa Scale for cohort studies. Results We screened titles and abstracts of 6711 records and full texts of 56 records. We included 12 studies: one RCT and 11 cohort studies. For prevention, the evidence is very uncertain about the effect of formula on neonatal hypoglycaemia (621 infants, OR 3.01 [0.53 to 17.13], p = 0.21, I 2 = 85%, very low certainty evidence). One RCT showed that formula may be more likely to correct neonatal hypoglycaemia than oral dextrose gel (222 infants, RR 1.27 [1.11 to 1.46], p = 0.0004, low certainty evidence). Conclusions The effect of formula in preventing neonatal hypoglycaemia remains uncertain; although formula may be more effective than oral dextrose gel in correcting hypoglycaemia. Trial Registration This review was registered in PROSPERO (CRD42024581348)
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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 | 0.011 | 0.029 |
| Meta-epidemiology (narrow) | 0.002 | 0.001 |
| Meta-epidemiology (broad) | 0.018 | 0.023 |
| Bibliometrics | 0.006 | 0.006 |
| Science and technology studies | 0.001 | 0.001 |
| Scholarly communication | 0.003 | 0.002 |
| Open science | 0.002 | 0.001 |
| Research integrity | 0.002 | 0.002 |
| Insufficient payload (model declined to judge) | 0.007 | 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".