RBC transfusion and necrotizing enterocolitis in very preterm infants: a multicenter observational study
Bibliographic record
Abstract
The causal relationship between Packed red blood cell (RBC) transfusion and necrotizing enterocolitis (NEC) remains uncertain. This study aims to provide an exploration of transfusion and NEC in very preterm infants. Using data from the Chinese Neonatal Network cohort study between 2019 and 2021, the analysis focused on very preterm infants (with a birth weight of < 1500 g or a gestational age of < 32 weeks) who developed NEC after receiving transfusions. The time interval between the prior transfusion and NEC was analyzed. An uneven distribution of the time interval implies an association of transfusion and NEC. Additionally, multivariable logistic analysis was conducted to detect the prognosis of defined transfusion-associated NEC(TANEC). Of the 16,494 infants received RBC transfusions, NEC was noted in 1281 (7.7%) cases, including 409 occurred after transfusion. Notably, 36.4% (149/409) of post-transfusion NEC occurred within 2 days after transfusion. The time interval distribution showed a non-normal pattern (Shapiro-Wilk test, W = 0.513, P < 0.001), indicating a possible link between transfusion and NEC. TANEC was defined as NEC occurred within 2 days after transfusion. Infants with TANEC had a higher incidence of death (adjusted OR 1.69; 95% CI 1.08 to 2.64), severe bronchopulmonary dysplasia (adjusted OR 2.03; 95% CI 1.41 to 2.91) and late-onset sepsis (adjusted OR 2.06; 95% CI 1.37 to 3.09) compared with infants without NEC after transfusion. Unevenly high number of NEC cases after RBC transfusions implies transfusion is associated with NEC. TANEC is associated with a poor prognosis. Further research is warranted to enhance our understanding of TANEC.
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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 | 0.002 | 0.004 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.001 | 0.001 |
| Bibliometrics | 0.001 | 0.002 |
| Science and technology studies | 0.001 | 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) | 0.001 | 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".