A Novel Integrated Clinical–Biochemical–Radiological and Sonographic Classification of Necrotizing Enterocolitis
Bibliographic record
Abstract
OBJECTIVE: To evaluate the sensitivity and specificity of clinical, laboratory, and radiological markers and the neonatologist-performed intestinal ultrasound (NP-IUS) for treatment interventions in preterm neonates who developed necrotizing enterocolitis (NEC). STUDY DESIGN: This was a case-control study of preterm neonates < 35 weeks with a diagnostic workup for NEC. The diagnostic workup included NP-IUS performed by trained neonatologists using a standard protocol, abdominal roentgenogram (AXR), and laboratory investigations. Intestinal ultrasound (IUS) performed by two neonatologists was standardized to detect 11 injury markers. AXRs were read independently by experienced pediatric radiologists. The investigators who retrospectively interpreted the IUS were blinded to the clinical and treatment outcomes. RESULTS: < 0.0001). CONCLUSION: In this cohort, a combination of specific IUS markers and clinical signs of instability, abdominal wall cellulitis, plus laboratory markers were diagnostic of NEC requiring interventions. KEY POINTS: · The diagnosis of necrotizing enterocolitis requires a combination of markers.. · The combination of specific ultrasound markers, clinical signs, and laboratory markers were diagnostic of NEC requiring intervention.. · The intestinal ultrasound performed by a trained neonatologist was the most sensitive diagnostic marker of NEC requiring surgical intervention..
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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 | 0.007 | 0.014 |
| Meta-epidemiology (narrow) | 0.001 | 0.000 |
| Meta-epidemiology (broad) | 0.001 | 0.001 |
| Bibliometrics | 0.003 | 0.001 |
| Science and technology studies | 0.000 | 0.001 |
| Scholarly communication | 0.002 | 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".