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Record W3049632030 · doi:10.3389/fneur.2020.00862

Respiratory Support of the Preterm Neonate: Lessons About Ventilation-Induced Brain Injury From Large Animal Models

2020· review· en· W3049632030 on OpenAlexaff
Kyra Y. Y. Chan, Stephanie Miller, Georg M. Schmölzer, Vanesa Stojanovska, Graeme R. Polglase

Bibliographic record

VenueFrontiers in Neurology · 2020
Typereview
Languageen
FieldMedicine
TopicNeonatal Respiratory Health Research
Canadian institutionsRoyal Alexandra HospitalUniversity of Alberta
FundersMedical Research CouncilState Government of VictoriaNational Health and Medical Research CouncilNational Heart Foundation of Australia
KeywordsVentilation (architecture)MedicineMechanical ventilationIntensive care medicineNeonatal intensive care unitAnesthesiaPediatrics

Abstract

fetched live from OpenAlex

Many preterm neonates require mechanical ventilation which increases the risk of cerebral inflammation and white matter injury in the immature brain. In this review, we discuss the links between ventilation and brain injury with a focus on the immediate period after birth, incorporating respiratory support in the delivery room and subsequent mechanical ventilation in the neonatal intensive care unit. This review collates insight from large animal models in which acute injurious ventilation and prolonged periods of ventilation have been used to create clinically relevant brain injury patterns. These models are valuable resources in investigating the pathophysiology of ventilation-induced brain injury and have important translational implications. We discuss the challenges of reconciling lung and brain maturation in commonly used large animal models. A comprehensive understanding of ventilation-induced brain injury is necessary to guide the way we care for preterm neonates, with the goal to improve their neurodevelopmental outcomes.

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 machine prediction

Teacher imitation

Not 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.

metaresearch head score (Codex)0.001
metaresearch head score (Gemma)0.002
Version: metacan-v3-hybrid-931329e0061cValidation status: machine_predicted_unvalidated
Candidate categoriesnone
Consensus categoriesnone
DomainCandidate signal: none · Consensus signal: none
Study designCandidate signal: Not applicable · Consensus signal: none
GenreCandidate signal: Review · Consensus signal: Review
Teacher disagreement score0.002
Threshold uncertainty score0.007

Distilled classifier scores by category (both heads)

CategoryCodexGemma
Metaresearch0.0010.002
Meta-epidemiology (narrow)0.0010.000
Meta-epidemiology (broad)0.0010.001
Bibliometrics0.0010.001
Science and technology studies0.0000.001
Scholarly communication0.0010.001
Open science0.0010.001
Research integrity0.0010.002
Insufficient payload (model declined to judge)0.0020.001

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 head0.103
GPT teacher head0.409
Teacher spread0.306 · 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 source (direct Gemma or distilled Codex), not a consensus.

The models applied no category: nothing in the taxonomy fit this work.
Study designNot applicable
Domainnot available
GenreReview

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

Citations35
Published2020
Admission routes1
Has abstractyes

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