A Quality Improvement Initiative to Reduce Necrotizing Enterocolitis in Very Preterm Infants
Bibliographic record
Abstract
OBJECTIVE: To reduce the incidence of necrotizing enterocolitis (NEC) among very preterm infants in the Calgary Health Region to ≤2% within 2 years. METHODS: A multidisciplinary team developed key drivers for NEC. Targeted interventions included strategies to increase mothers' own milk (MOM), improve compliance with feeding regimens, standardize management of feeding intolerance, prevent intestinal microbial aberrations, and feed conservatively during blood transfusion and the treatment of patent ductus arteriosus. The outcome measure was NEC (≥ stage 2). Changes in NEC rates were examined among racial and ethnic groups. Process measures included MOM feeding at discharge, the difference between actual and expected time to reach full feeds, lowest hemoglobin, and the duration of empirical antibiotics. Growth, the rate of blood transfusion, and the duration of parenteral nutrition were balancing measures. The preintervention, intervention, and sustainment periods were January 2013 to June 2016, July 2016 to December 2018, and December 2018 to December 2021, respectively. RESULTS: We included 2787 infants born at ≤326/7 weeks' gestation (1105 preintervention, 763 during intervention, and 919 in sustainment). NEC decreased from 5.6% to 1.9%. Process measures indicated increased MOM feeding at discharge, improved compliance with feeding regimens, increased lowest hemoglobin levels, and shorter durations of empirical antibiotics. Balancing measures revealed improved weight Z-scores, shorter durations on parenteral nutrition, and increased rates of blood transfusion. CONCLUSIONS: Quality improvement initiatives to increase MOM, improve compliance with feeding regimens, feed conservatively during blood transfusion and treatment of patent ductus arteriosus, and prevent intestinal microbial aberrations were associated with reduced 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 | 0.014 | 0.030 |
| Meta-epidemiology (narrow) | 0.001 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.001 |
| Bibliometrics | 0.002 | 0.002 |
| Science and technology studies | 0.001 | 0.000 |
| Scholarly communication | 0.001 | 0.001 |
| Open science | 0.001 | 0.003 |
| Research integrity | 0.001 | 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".