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Record W3210228627 · doi:10.3390/children8110983

Impact of a “Brain Protection Bundle” in Reducing Severe Intraventricular Hemorrhage in Preterm Infants <30 Weeks GA: A Retrospective Single Centre Study

2021· article· en· W3210228627 on OpenAlexaff
Nishkal Persad, Edmond Kelly, Nely Amaral, Angela Neish, Courtney Cheng, Chun‐Po Steve Fan, Kyle Runeckles, Vibhuti Shah

Bibliographic record

VenueChildren · 2021
Typearticle
Languageen
FieldMedicine
TopicNeonatal and fetal brain pathology
Canadian institutionsTed Rogers Centre for Heart ResearchUniversity Health NetworkMount Sinai Hospital
Fundersnot available
KeywordsMedicineIntraventricular hemorrhageIncidence (geometry)Retrospective cohort studyIntensive careDemographicsPediatricsCohortGestational ageSurgeryIntensive care medicineInternal medicinePregnancy

Abstract

fetched live from OpenAlex

BACKGROUND: despite advances in perinatal care, periventricular/intraventricular hemorrhage (IVH) continues to remain high in neonatal intensive care units (NICUs) worldwide. Studies have demonstrated the benefits of implementing interventions during the antenatal period, stabilization after birth (golden hour management) and postnatally in the first 72 h to reduce the incidence of IVH. OBJECTIVE: to compare the incidence of severe intraventricular hemorrhage (IVH ≥ Grade III) before and after implementation of a "brain protection bundle" in preterm infants <30 weeks GA. STUDY DESIGN: a pre- and post-implementation retrospective cohort study to compare the incidence of severe IVH following execution of a "brain protection bundle for the first 72 h from 2015 to 2018. Demographics, management practices at birth and in the NICU, cranial ultrasound results and short-term morbidities were compared. RESULTS: = 0.37) was observed on the first cranial scan performed after 72 h of age. CONCLUSION: the implementation of a "brain protection bundle" was not effective in reducing the incidence of severe IVH within the first 72 h of life in our centre.

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)0.001
Version: codex-gemma-dda1882f352aValidation status: machine_predicted_unvalidated
Candidate categoriesnone
Consensus categoriesnone
DomainCandidate signal: none · Consensus signal: none
Study designCandidate signal: Observational · Consensus signal: Observational
GenreCandidate signal: Empirical · Consensus signal: Empirical
Teacher disagreement score0.044
Threshold uncertainty score0.735

Codex and Gemma teacher scores by category

CategoryCodexGemma
Metaresearch0.0000.001
Meta-epidemiology (narrow)0.0000.000
Meta-epidemiology (broad)0.0010.000
Bibliometrics0.0000.001
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 head0.009
GPT teacher head0.249
Teacher spread0.240 · 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

Citations15
Published2021
Admission routes1
Has abstractyes

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