MétaCan
Menu
Back to cohort
Record W2999808230 · doi:10.3390/healthcare8010017

Chest Compression in Neonatal Cardiac Arrest: Cerebral Blood Flow Measurements in Experimental Models

2020· review· en· W2999808230 on OpenAlexafffund
Anne Lee Solevåg, Po‐Yin Cheung, Georg M. Schmölzer

Bibliographic record

VenueHealthcare · 2020
Typereview
Languageen
FieldMedicine
TopicCardiac Arrest and Resuscitation
Canadian institutionsRoyal Alexandra HospitalUniversity of Alberta
FundersHeart and Stroke Foundation of Canada
KeywordsCerebral blood flowVentilation (architecture)MedicineCarotid arteriesInternal carotid arteryBlood flowAnesthesiaCardiologyInternal medicine

Abstract

fetched live from OpenAlex

The main aim of this paper was to provide an overview of studies that measured cerebral blood flow (CBF), directly or indirectly, during chest compression (CC) in neonatal animals. Our main research question was: how did different ways of performing CC influence CBF. We also aimed to discuss strengths and limitations of different methods for measuring CBF. Based on a search in Medline Ovid, we identified three studies in piglets that investigated different CC:ventilation (C:V) ratios, as well as three piglet studies investigating continuous CC with asynchronous ventilation. CBF was measured indirectly in all studies by means of carotid artery (CA) flow and regional cerebral oxygenation (rcSO2). The CA provides flow to the brain, but also to extracerebral structures. The relative sizes of the internal and external carotid arteries and their flow distributions are species-dependent. rcSO2 is a non-invasive continuous measure, but does not only reflect CBF, but also cerebral blood volume and the metabolic rate of oxygen in the brain. Continuous CC with asynchronous ventilation at a CC rate of 120/min, and combining CC with a sustained inflation (four studies in piglets and one in lambs) provided a faster CBF recovery compared with the standard 3:1 C:V approach.

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 categoriesMeta-epidemiology (narrow)
Consensus categoriesnone
DomainCandidate signal: none · Consensus signal: none
Study designCandidate signal: Other design · Consensus signal: none
GenreCandidate signal: Review · Consensus signal: Review
Teacher disagreement score0.942
Threshold uncertainty score1.000

Codex and Gemma teacher scores by category

CategoryCodexGemma
Metaresearch0.0000.000
Meta-epidemiology (narrow)0.0010.000
Meta-epidemiology (broad)0.0020.001
Bibliometrics0.0000.001
Science and technology studies0.0000.000
Scholarly communication0.0000.000
Open science0.0000.000
Research integrity0.0010.001
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 head0.112
GPT teacher head0.376
Teacher spread0.265 · 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.

Study designOther design
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

Citations1
Published2020
Admission routes2
Has abstractyes

Explore more

Same venueHealthcareSame topicCardiac Arrest and ResuscitationFrench-language works237,207