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Record W2765852629 · doi:10.1115/1.4038221

Preliminary Design and Testing of a Cavo-Arterial Pump Utilizing Axial Magnetic Couplings

2017· article· en· W2765852629 on OpenAlexaff
John Valdovinos, J. Christopher Bouwmeester, Pramod Bonde

Bibliographic record

VenueJournal of Medical Devices · 2017
Typearticle
Languageen
FieldEngineering
TopicMechanical Circulatory Support Devices
Canadian institutionsUniversity of Toronto
FundersAmerican Heart Association
KeywordsImpellerCirculatory systemTorqueInductive couplingVolumetric flow rateCentrifugal pumpMaterials scienceBiomedical engineeringMechanicsCardiologyPhysicsEngineeringMedicineElectrical engineering

Abstract

fetched live from OpenAlex

Right ventricular (RV) dysfunction has limited the effectiveness of mechanical circulatory support (MCS) therapy in some heart failure (HF) patients. Intravascular pumps can provide adequate circulatory support without the need for extensive operations. The development of an intravascular right ventricular assist device (RVAD), called the cavo-arterial pump (CAP), is presented. Two prototypes of the CAP were developed to demonstrate the feasibility of providing adequate pulmonary support and to demonstrate the feasibility of using axial magnetic couplings for contactless torque transmission from the motor shaft to the pump impeller. The CAP utilizing a direct drive mechanism produced a maximum pressure of 100 mm Hg and a maximum flow of 2.25 L/min when operated at 24 kRPM. When a magnetic drive mechanism was used, the overall flowrate decreased due to a loss in torque transmission. The magnetic drive CAP was able to operate up to 18.5 kRPM and produce a maximum flowrate of 1.35 L/min and a maximum pressure difference of 40 mm Hg. These results demonstrate that the CAP produces sufficient output for partial circulatory support of the pulmonary circulation, and that axial magnetic couplings can help to eliminate the sealing system needed to isolate the miniature motor and bearings from blood contact.

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)0.002
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 score0.786
Threshold uncertainty score0.545

Codex and Gemma teacher scores by category

CategoryCodexGemma
Metaresearch0.0020.002
Meta-epidemiology (narrow)0.0000.000
Meta-epidemiology (broad)0.0000.000
Bibliometrics0.0000.000
Science and technology studies0.0000.000
Scholarly communication0.0000.000
Open science0.0010.000
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 head0.043
GPT teacher head0.273
Teacher spread0.230 · 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

Citations1
Published2017
Admission routes1
Has abstractyes

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