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Haemodynamic changes with varying chest compression rates in asphyxiated piglets

2022· article· en· W4210876885 on OpenAlexaff
Marlies Bruckner, Mattias Neset, Megan O’Reilly, Tze-Fun Lee, Po‐Yin Cheung, Georg M. Schmölzer

Bibliographic record

VenueArchives of Disease in Childhood Fetal & Neonatal · 2022
Typearticle
Languageen
FieldMedicine
TopicCardiac Arrest and Resuscitation
Canadian institutionsRoyal Alexandra HospitalUniversity of Alberta
FundersLaerdal Foundation for Acute Medicine
KeywordsMedicineHemodynamicsHeart rateAnesthesiaCardiopulmonary resuscitationCardiac outputStroke volumeVentricleBlood pressureResuscitationBlood flowCardiologyInternal medicine

Abstract

fetched live from OpenAlex

Background Current neonatal resuscitation guidelines recommend that chest compressions (CCs) be delivered at a rate of 90/min. The aim of the study was to investigate the haemodynamic effects of different CC rates in a neonatal piglet model. Methods Six asphyxiated piglets were randomised to CC with rates of 60/min, 90/min, 120/min, 150/min and 180/min for 1 min at each rate. CCs superimposed with sustained inflations were performed with an automated CC machine. Results Six newborn piglets (age 0–3 days, weight 2.0–2.3 kg) were included in the study. Overall, there was a gradual increase in stroke volume, minimum and maximum rate of left ventricle pressure change (dp/dtmin and dp/dtmax), and carotid blood flow until CC rate of 150/min, with a level-off effect at a CC rate of 180/min. However, cardiac output continued to increase with the highest being at a CC rate of 180/min. Conclusion Rate of CC was associated with changes in haemodynamic parameters during cardiopulmonary resuscitation. CC rate of 150–180/min during CC resulted in the highest cardiac output and arterial blood pressure. Trial registration number Preclincialtrials.eu PCTE0000249.

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)0.001
Version: metacan-v3-hybrid-931329e0061cValidation status: machine_predicted_unvalidated
Candidate categoriesnone
Consensus categoriesnone
DomainCandidate signal: none · Consensus signal: none
Study designCandidate signal: Bench or experimental · Consensus signal: Bench or experimental
GenreCandidate signal: Empirical · Consensus signal: Empirical
Teacher disagreement score0.001
Threshold uncertainty score0.005

Distilled classifier scores by category (both heads)

CategoryCodexGemma
Metaresearch0.0010.001
Meta-epidemiology (narrow)0.0000.000
Meta-epidemiology (broad)0.0010.000
Bibliometrics0.0000.000
Science and technology studies0.0000.001
Scholarly communication0.0000.000
Open science0.0000.000
Research integrity0.0000.001
Insufficient payload (model declined to judge)0.0010.000

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 head0.007
GPT teacher head0.243
Teacher spread0.236 · 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 designBench or experimental
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
Published2022
Admission routes1
Has abstractyes

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