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Record W4407840034 · doi:10.3390/children12030264

Sustained Reduction in Severe Intraventricular Hemorrhage in Micropremature Infants: A Quality Improvement Intervention

2025· article· en· W4407840034 on OpenAlexaff
Sabrina E. Wong, Lisa Sampson, Michael Dunn, Asaph Rolnitsky

Bibliographic record

VenueChildren · 2025
Typearticle
Languageen
FieldMedicine
TopicNeonatal and fetal brain pathology
Canadian institutionsUniversity of TorontoHealth Sciences CentreSunnybrook Health Science Centre
Fundersnot available
KeywordsMedicineIntraventricular hemorrhagePediatricsNecrotizing enterocolitisPopulationPeriventricular leukomalaciaPsychological interventionGestational agePregnancy

Abstract

fetched live from OpenAlex

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.

Fetched live from OpenAlex and de-inverted. Abstracts are not stored in this database: the inverted indexes are 8.6 GB of the frame’s 9.3 GB of text, and the host has 13 GB free.

How this classification was reachedexpand

Full frame distilled prediction

Teacher imitation

Not calibrated prevalence, not ground truth. Human validation pending. Learned from the 10,348 direct Codex labels and 10,348 direct Gemma labels. Candidate is the union of thresholded teacher heads; consensus is their intersection. These outputs are machine_predicted_unvalidated and are not human labels or direct frontier model labels.

metaresearch head score (Codex)0.000
metaresearch head score (Gemma)0.000
Version: codex-gemma-dda1882f352aValidation status: machine_predicted_unvalidated
Candidate categoriesnone
Consensus categoriesnone
DomainCandidate signal: none · Consensus signal: none
Study designCandidate signal: Observational · Consensus signal: none
GenreCandidate signal: Empirical · Consensus signal: Empirical
Teacher disagreement score0.778
Threshold uncertainty score0.461

Codex and Gemma teacher scores by category

CategoryCodexGemma
Metaresearch0.0000.000
Meta-epidemiology (narrow)0.0000.000
Meta-epidemiology (broad)0.0000.000
Bibliometrics0.0000.000
Science and technology studies0.0000.000
Scholarly communication0.0000.000
Open science0.0000.000
Research integrity0.0000.000
Insufficient payload (model declined to judge)0.0000.000

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.

Opus teacher head0.004
GPT teacher head0.265
Teacher spread0.261 · how far apart the two teachers sit on this one work
Validation statusscore_only:v0-immature-baseline · verbatim from the scoring run: score_only means the number may rank works, and no category label ships from it

Classification

machine, unvalidated

Machine predicted; a candidate call from one teacher head, not a consensus.

The models applied no category: nothing in the taxonomy fit this work.
Study designObservational
Domainnot available
GenreEmpirical

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".

Quick stats

Citations3
Published2025
Admission routes1
Has abstractyes

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