Early Biomarkers in Neonatal Necrotizing Enterocolitis: A Pilot Study
Bibliographic record
Abstract
BACKGROUND: Necrotizing enterocolitis (NEC) is a frequent serious disease of the digestive system in neonates. It is considered as an important cause of serious neonatal complication and death. Therefore, its early suspicion and proper management are important. AIM: Early and sensitive detection of neonatal NEC through determination of levels of fecal calprotectin (FCP), serum levels of procalcitonin (PCT), high-sensitivity C-reactive protein (hs-CRP), epithelial neutrophil activating peptide-78 (ENA-78), and interleukin 18 (IL-18). METHOD: This prospective case control study was conducted in Tanta University Hospital from June 2016 to March 2018. The study included 20 healthy neonates (control group) and 20 NEC newborn patients. They were all subjected to the measurement of levels of FCP and serum levels of hs-CRP, PCT, ENA-78, IL-18, Malondialdehyde (MDA), and total antioxidant capacity (TAC). Receiver operating characteristic (ROC) curve analysis was conducted for FCP, ENA-78, PCT, hs-CRP, and IL-18. RESULTS: The study found a detectable increase in FCP level and serum levels of hs-CRP, PCT, ENA-78, IL-18, and MDA in NEC group in comparison to their levels in the control group. Also, it found a detectable decline in the levels of TAC in comparison to its level in the control group. CONCLUSION: FCP, ENA-78, and PCT can be considered as early markers for diagnosis of NEC.
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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 | 0.001 | 0.002 |
| Meta-epidemiology (narrow) | 0.001 | 0.000 |
| Meta-epidemiology (broad) | 0.001 | 0.000 |
| Bibliometrics | 0.001 | 0.000 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.000 | 0.001 |
| Open science | 0.000 | 0.000 |
| 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".