Transfusion Associated Necrotizing Enterocolitis: A Meta-analysis of Observational Data
Bibliographic record
Abstract
BACKGROUND AND OBJECTIVES: Several studies have reported the possibility of an association between recent exposure to transfusion and development of necrotizing enterocolitis (NEC). Our objective was to systematically review and meta-analyze the association between transfusion and NEC (TANEC), identify predictors of TANEC, and the assess impact of TANEC on outcomes. METHODS: Medline, Embase, CINAHL, and bibliographies of identified articles were searched for studies assessing association with recent (within 48 hours) exposure to transfusion and NEC. Two reviewers independently collected data and assessed the quality of the studies for bias in sample selection, exposure assessment, confounders, analyses, outcome assessments, and attrition. Meta-analyses were performed by using random effect model, and odds ratio and 95% confidence interval were calculated. RESULTS: Eleven retrospective case-control studies and 1 cohort study of moderate risk of bias were included. Ten case-control studies had NEC not associated with transfusion as control patients (unmatched). Recent exposure to transfusion was associated with NEC. Neonates who developed TANEC were younger by 1.5 weeks, were of 528 g lower birth weight, were more likely to have patent ductus arteriosus, and were more likely receiving ventilatory support. TANEC infants had higher risk of mortality. Two pre-post comparative studies of 20 patients reported reduction of TANEC after withholding feeds during transfusion. CONCLUSIONS: Recent exposure to transfusion was associated with NEC in neonates. Neonates who developed TANEC were at overall higher risk of NEC. TANEC patients were at higher risk of mortality, but additional studies adjusting for confounders are needed.
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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 | 0.047 |
| Meta-epidemiology (narrow) | 0.002 | 0.001 |
| Meta-epidemiology (broad) | 0.014 | 0.044 |
| Bibliometrics | 0.006 | 0.008 |
| Science and technology studies | 0.001 | 0.001 |
| Scholarly communication | 0.003 | 0.001 |
| Open science | 0.002 | 0.001 |
| Research integrity | 0.002 | 0.002 |
| Insufficient payload (model declined to judge) | 0.002 | 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".