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Record W4390937501 · doi:10.1016/j.jvssci.2024.100190

Technical and analytical approach to biventricular pressure-volume loops in swine including a completely endovascular, percutaneous closed-chest large animal model

2024· article· en· W4390937501 on OpenAlexaff
David P. Stonko, Mathieu Rousseau, Colin Price, Amy Benike, Rebecca N. Treffalls, Nichole Brunton, D.T. Rosen, Jonathan J. Morrison

Bibliographic record

VenueJVS Vascular Science · 2024
Typearticle
Languageen
FieldMedicine
TopicHemodynamic Monitoring and Therapy
Canadian institutionsUniversité de Montréal
Fundersnot available
KeywordsPercutaneousMedicineInferior vena cavaRadiologyCardiologyStroke volumeOcclusionClosed loopInternal medicineHeart failureEjection fraction

Abstract

fetched live from OpenAlex

Pressure-volume (PV) loop analysis is a sophisticated invasive approach to quantifying load-dependent and independent measures of cardiac function. Biventricular (BV) PV loops allow left and right ventricular function to be quantified simultaneously and independently, which is important for conditions and certain physiologic states, such as ventricular decoupling or acute physiologic changes. BV PV loops can be performed in an entirely endovascular, percutaneous, and closed-chest setting. This technique is helpful in a survival animal model, as a percutaneous monitoring system during endovascular device experiments, or in cases where chest wall compliance is being tested or may be a confounder. In this article, we describe the end-to-end implementation of a completely endovascular, totally percutaneous, and closed-chest large animal model to obtain contemporaneous BV PV loops in 40 to 70 kg swine. We describe the associated surgical and technical challenges and our solutions to obtaining endovascular BV PV loops, closed-chest cardiac output, and stroke volume (including validation of the correction factor necessary for thermodilution), as well as how to perform endovascular inferior vena cava occlusion in this swine model. We also include techniques for data acquisition and analysis that are required for this method.

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)0.002
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: Simulation or modeling · Consensus signal: none
GenreCandidate signal: Empirical · Consensus signal: Empirical
Teacher disagreement score0.896
Threshold uncertainty score0.949

Codex and Gemma teacher scores by category

CategoryCodexGemma
Metaresearch0.0020.000
Meta-epidemiology (narrow)0.0000.000
Meta-epidemiology (broad)0.0010.000
Bibliometrics0.0010.002
Science and technology studies0.0000.000
Scholarly communication0.0000.000
Open science0.0000.000
Research integrity0.0000.001
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 head0.032
GPT teacher head0.307
Teacher spread0.276 · 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 designSimulation or modeling
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

Citations4
Published2024
Admission routes1
Has abstractyes

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