Sustained Reduction in Severe Intraventricular Hemorrhage in Micropremature Infants: A Quality Improvement Intervention
Bibliographic record
Abstract
BACKGROUND: Quality improvement (QI) interventions may reduce the incidence and severity of Intraventricular Hemorrhage (IVH) in the population of inborn micropremature infants (born at ≤26 weeks' gestation) with the goal of improving outcomes in this high-risk population. METHODS: A multidisciplinary team reviewed the current literature to develop a site-specific brain protective bundle. Baseline data were collected from June 2014 to February 2015, with interventions occurring from March 2015 to December 2015. The period of sustainability was assessed from January 2016 to December 2023. Control charts were used to analyze the effect of the interventions. Outcome measures included all grades of IVH, periventricular leukomalacia (PVL), necrotizing enterocolitis (NEC), and spontaneous intestinal perforations (SIP). RESULTS: Brain care initiatives decrease the rate of severe IVH in the inborn micropremature infant population from a baseline of 21% to 6.45% with a sustained rate of 4.5% with no change to balancing measures. CONCLUSIONS: Brain-protective initiatives such as midline head positioning and minimal handling are associated with a significant and sustained reduction in severe IVH among inborn micropremature infants.
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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 | 0.019 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.001 |
| Bibliometrics | 0.001 | 0.001 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.001 | 0.000 |
| Open science | 0.001 | 0.001 |
| Research integrity | 0.000 | 0.001 |
| 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".