1: Very Preterm Infants with Necrotizing Enterocolitis and Sepsis Demonstrate Slower Brain Metabolic Development
Bibliographic record
Abstract
Very preterm infants are predisposed to postnatal infections and necrotizing enterocolitis (NEC) that are associated with poor outcome and increased risk of brain injury. To assess brain metabolic development in infants exposed to neonatal infections and NEC using indices of neuronal integrity (N-acetyl aspartate [NAA]/choline), measured with magnetic resonance spectroscopy (MRS). Hypothesis: NEC with concurrent sepsis is associated with impaired brain development, as reflected by NAA/choline ratios. A total of 213 preterm born neonates (gestational age 24 to 32 weeks) recruited from two hospitals underwent MRS in the first weeks of life (32 weeks) and term-equivalent age (41 weeks). Ratios of NAA to choline were calculated from the basal ganglia. Data were categorized into six groups: preterm controls with and without brain injury, clinical infection, culture positive infection, NEC diagnosis with and without sepsis. A generalized linear model was used to assess the change in NAA/choline from scan 1 to scan 2 across groups (divided by the difference between ages at scan), adjusted for gestational age at birth and site. Post-hoc between-group comparisons were Bonferroni corrected (P<0.05). The groups were composed of the following number of infants: 51 with brain injury, 31 without brain injury, 28 had clinical infection, 61 had sepsis, 17 had NEC without sepsis and 25 had NEC with sepsis. The change in NAA/choline from scan 1 to 2 was significantly different between groups (P=0.04). Post-hoc comparisons revealed the rate of NAA/choline change was significantly lower in infants with NEC and concurrent sepsis in comparison to controls without injury (P=0.01). Infants with NEC and additional sepsis are at high risk for adverse metabolic brain development. This work highlights the importance of the prevention of NEC and sepsis.
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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 | 0.000 | 0.003 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.000 | 0.000 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.000 | 0.000 |
| Open science | 0.000 | 0.001 |
| Research integrity | 0.000 | 0.000 |
| Insufficient payload (model declined to judge) | 0.003 | 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".