Zinc Supplementation and the Prevention and Treatment of Sepsis in Young Infants: A Systematic Review and Meta-Analysis
Bibliographic record
Abstract
BACKGROUND: Prematurity and low birth weight are major risk factors for neonatal sepsis. Zinc supplements have been previously shown to be beneficial in pregnancy and small for gestational age birth outcomes. There is sparse information, however, on the potential benefits of zinc supplementation to prevent or treat serious infections in this age group. OBJECTIVE: The aim of this study was to assess the efficacy of preventive and therapeutic zinc supplementation in young infant (<4 months) sepsis. METHODS: MEDLINE, Cochrane CENTRAL, and other databases were searched from inception until 18 June 2021. Studies assessing preventive and therapeutic zinc supplementation in young infants in relation to incidence and outcomes of suspected sepsis were included. Meta-analyses of pooled effects were calculated for sepsis-related outcomes. RESULTS: Nine randomized controlled trials involving 2,553 infants were included. Six studies reported therapeutic efficacy, whereas 3 evaluated preventive benefits of zinc supplementation. Preventive studies suggest a protective effect of zinc supplementation on neonatal mortality rate (NMR) (risk ratio (RR) 0.28; 95% CI 0.12-0.67, LOW certainty), but with no effect on the incidence of sepsis, both in preterm neonates. Among young infants, therapeutic zinc was associated with significant reductions in treatment failure (RR 0.61; 95% CI 0.44-0.85; MODERATE certainty) and further subgroup analysis showing significant reduction in infant mortality rate with 3 mg/kg/twice a day dosage only (RR 0.49; 95% CI 0.27-0.87, LOW certainty). Therapeutic zinc supplementation in neonates did not show any effect on hospital stay or NMR. CONCLUSION: Zinc supplementation could potentially reduce mortality and treatment failure in young infants but has no noteworthy influence on hospital stay and in the prevention of sepsis. Further studies with larger sample sizes are needed to confirm the direction and magnitude of effects if any.
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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 | 0.001 | 0.000 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.006 | 0.001 |
| Bibliometrics | 0.000 | 0.001 |
| 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) | 0.000 | 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".