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Record W2744565563 · doi:10.1299/jsmemecjo.2004.7.0_39

Analysis of Blood Flow in Aorta with Ultrasonic-Measurement-Integrated Simulation

2004· article· en· W2744565563 on OpenAlexaff
Kenichi Funamoto, Toshiyuki Hayase, Yoshifumi Saijo, Tomoyuki Yambe

Bibliographic record

VenueThe proceedings of the JSME annual meeting · 2004
Typearticle
Languageen
FieldMedicine
TopicHemodynamic Monitoring and Therapy
Canadian institutionsInstitute of Aging
Fundersnot available
KeywordsBlood flowUltrasonic sensorComputationAortaCirculatory systemDescending aortaFlow (mathematics)Computer scienceComputer simulationBlood pressureUltrasonic flow meterBiomedical engineeringAcousticsSimulationCardiologyMedicineRadiologyPhysicsMechanicsAlgorithm

Abstract

fetched live from OpenAlex

In order to reproduce the blood velocity and pressure fields of the blood flow for an accurate diagnosis or treatment for serious circulatory diseases such as aortic aneurysms, we are developing a new analysis methodology, namely Ultrasonic-Measurement-Integrated (UMI) simulation, by integrating color Doppler ultrasonography and numerical simulation. In the UMI simulation, feedback signals are added to the governing equations to compensate for the difference between computation and measurement. In this paper, we perform UMI simulation for the blood flow in descending aorta with an aneurysm. It is revealed that the UMI simulation reproduces the complicated blood flow structure and detects the incorrect measurement data.

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: Bench or experimental · Consensus signal: none
GenreCandidate signal: Empirical · Consensus signal: Empirical
Teacher disagreement score0.210
Threshold uncertainty score0.292

Codex and Gemma teacher scores by category

CategoryCodexGemma
Metaresearch0.0010.000
Meta-epidemiology (narrow)0.0000.000
Meta-epidemiology (broad)0.0000.000
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 head0.013
GPT teacher head0.252
Teacher spread0.239 · 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 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

Citations0
Published2004
Admission routes1
Has abstractyes

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