Early Enteral Nutrition and Clinical Outcomes in Critically Ill Pediatric Populations: A Systematic Review and Meta-Analysis
Bibliographic record
Abstract
OBJECTIVE: Guidelines recommend implementing early enteral nutrition (EN) (EEN) in critically ill children. The aim of the study was to determine if EEN for critically ill children is associated with improved clinical outcomes compared with delayed enteral nutrition (DEN), prioritizing associations adjusted for severity of illness. PROSPERO (CRD42021286271). DATA SOURCES: MEDLINE, Embase, CINAHL, and CENTRAL databases to October 2024. STUDY SELECTION: The population was critically ill children, the intervention was EEN, the comparator was DEN, the outcome was mortality or clinical outcomes, and the study designs included randomized control trials (RCTs), quasi-experimental, observational cohort, or case-control. DATA EXTRACTION: Screening, extraction, and risk of bias assessment using the Newcastle-Ottawa Scale and Cochrane Risk of Bias and Grading of Recommendations Assessment, Development, and Evaluation (GRADE) assessment were conducted in duplicate by two reviewers. Eighteen of 8478 screened studies were included. DATA SYNTHESIS: Mortality outcomes were pooled and meta-analyzed using random-effects models. Secondary outcomes were described qualitatively, and directions of associations were reported. Thirteen studies (1 RCT, 12 cohort) reported mortality; however, only three adjusted for illness severity. In the adjusted analysis, receiving EEN was associated with reduced mortality (adjusted odds ratio 0.36 (95% CI, 0.14-0.91), I2 = 78.6%, n = 5864). The certainty of evidence, as assessed by GRADE, was very low due to indirectness. In the qualitative review of 18 studies (1 RCT, 17 cohort studies, n = 9829), EEN had an association with reduced length of stay, length of invasive respiratory support, improved nutrition adequacy, reduced maximum pediatric logistic organ dysfunction score, and infection. No harmful effects of EEN were found after adjusting for confounding variables. CONCLUSIONS: EEN was associated with beneficial outcomes. However, the inclusion of mostly cohort studies with limited confounding adjustment, the small number of studies, the presence of between-study heterogeneity and residual confounding, and heterogeneity in measured outcomes and assessment methods resulted in very low certainty of evidence.
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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 | 0.023 | 0.052 |
| Meta-epidemiology (narrow) | 0.003 | 0.001 |
| Meta-epidemiology (broad) | 0.020 | 0.035 |
| Bibliometrics | 0.007 | 0.009 |
| Science and technology studies | 0.001 | 0.001 |
| Scholarly communication | 0.004 | 0.002 |
| Open science | 0.002 | 0.002 |
| Research integrity | 0.002 | 0.003 |
| Insufficient payload (model declined to judge) | 0.004 | 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".