A Quality Improvement Intervention to Reduce Necrotizing Enterocolitis in premature infants with Probiotic Supplementation
Bibliographic record
Abstract
Necrotizing Enterocolitis (NEC) is a severe intestinal inflammatory disease due to multifactorial causes that present in preterm infants. Compared with similar neonatal intensive care units, our NEC rate was increasing and prompted reduction by a quality improvement (QI) intervention. METHODS: probiotic. We used education, process mapping, process control statistics, and forcing mechanism to implement the changes. In addition to reducing NEC rates, our additional outcome measures were sepsis, mortality, sepsis evaluations, feeding intolerance, growth, days of both antimicrobials, and parenteral nutrition use. Process measures were compliance with probiotics supplementation policy and balancing measures were sepsis rates and feeding intolerance. RESULTS: NEC rates decreased from 4.4% to the current 1.7%, and in a pre/post-intervention analysis, the results were significant in all patient subcategories. We did not demonstrate a reduction in mortality. No adverse events occurred. Feeding intolerance episodes and days nil-per-os decreased with no differences in growth at discharge. These results continued over 2 years, and this practice has already spread to several neonatal intensive care units in Ontario, Canada. CONCLUSIONS: We utilized QI methods and tools to implement a successful practice change of routine probiotic supplementation to reduce NEC rates in preterm infants. We suggest considering this intervention as a successful means to prevent this serious illness.
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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 | 0.003 | 0.013 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.000 | 0.000 |
| Science and technology studies | 0.001 | 0.000 |
| Scholarly communication | 0.000 | 0.000 |
| Open science | 0.001 | 0.001 |
| Research integrity | 0.000 | 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".