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Record W4210306244 · doi:10.1308/rcsann.2021.0258

Non-invasive cardiac output monitoring with electrical velocimetry after cardiac surgery in infants

2022· article· en· W4210306244 on OpenAlexaff
Sashini Iddawela, Sumbal Naseem, John Stickley, Phil Botha, NE Khan, DJ Barron, TJ Jones, NE Drury

Bibliographic record

VenueAnnals of The Royal College of Surgeons of England · 2022
Typearticle
Languageen
FieldMedicine
TopicHemodynamic Monitoring and Therapy
Canadian institutionsHospital for Sick ChildrenUniversity of Toronto
Fundersnot available
KeywordsMedicineInterquartile rangeCardiac indexCardiac surgeryAnesthesiaIntensive care unitBlood pressureVelocimetryIntensive careCardiac outputSurgeryInternal medicineIntensive care medicine

Abstract

fetched live from OpenAlex

INTRODUCTION: Low cardiac output following cardiac surgery is a major determinant of outcome that may be improved by early detection, yet there are no widely accepted methods for its measurement in young children. We evaluated the feasibility of the routine use of electrical velocimetry, a non-invasive technique providing continuous measurement of cardiac output, in infants in the early postoperative period. METHODS: With ethical approval and parental consent, infants undergoing cardiac surgery were recruited. The ICON electrical velocimetry monitor was attached on admission to the intensive care unit (ICU) and remained for up to 24h. RESULTS: =0.02) and no correlation with blood pressure, central venous pressure or arteriovenous oxygen difference. Data were recorded for a median of 19h (range 5-24), with lead detachment or movement artefact the most common causes of data loss. There was marked minute-to-minute variability, with 25% of consecutive measurements having >5% variability. CONCLUSION: Cardiac index measured by electrical velocimetry in infants in the early postoperative period is impaired by frequent data loss and marked intrapatient variability. Our feasibility study suggests that it is unsuitable for use as a routine monitoring tool in the setting of postsurgical ICU care.

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)0.001
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: Observational
GenreCandidate signal: Empirical · Consensus signal: Empirical
Teacher disagreement score0.048
Threshold uncertainty score0.587

Codex and Gemma teacher scores by category

CategoryCodexGemma
Metaresearch0.0010.000
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 head0.019
GPT teacher head0.258
Teacher spread0.238 · 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

Citations1
Published2022
Admission routes1
Has abstractyes

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