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Record W4385483720 · doi:10.54254/2753-8818/6/20230216

The influence of vortices on hemodynamics in blood vessels

2023· article· en· W4385483720 on OpenAlex

Why this work is in the frame

A frame that forgets how it found something cannot be audited. These are the routes that admitted this work.

affAt least one author lists a Canadian institution in the pinned OpenAlex snapshot.

Bibliographic record

VenueTheoretical and Natural Science · 2023
Typearticle
Languageen
FieldMedicine
TopicCardiovascular Health and Disease Prevention
Canadian institutionsMcMaster University
Fundersnot available
KeywordsVortexBlood flowParticle image velocimetryHemodynamicsMechanicsComputational fluid dynamicsShear stressFlow (mathematics)Biomedical engineeringCardiologyPhysicsMaterials scienceMedicineTurbulence

Abstract

fetched live from OpenAlex

Blood flow in vessels is affected by several factors like vessel shape, blood thickness, and heart function. Swirling patterns of flow, called vortices, are often seen in blood vessels and can affect how blood flows. This study aims to understand how vortices affect blood flow and the reasons behind these changes. Different instruments, like particle image velocimetry (PIV), computational fluid dynamics (CFD), and magnetic resonance imaging (MRI), were used to measure and analyze blood flow. CFD simulations were done using realistic blood vessel models to study how vortices form and how they affect blood velocity and pressure. The results show that vortices can cause significant changes in blood velocity and pressure, which can lead to changes in blood flow. The increased wall shear stress may contribute to the development of heart disease. This research highlights the importance of considering the impact of vortices on blood flow dynamics when designing and assessing cardiovascular devices and treatments.

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.

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)0.001
Version: codex-gemma-dda1882f352aValidation status: machine_predicted_unvalidated
Candidate categoriesnone
Consensus categoriesnone
DomainCandidate signal: none · Consensus signal: none
Study designCandidate signal: Theoretical or conceptual · Consensus signal: none
GenreCandidate signal: Empirical · Consensus signal: Empirical
Teacher disagreement score0.942
Threshold uncertainty score0.420

Codex and Gemma teacher scores by category

CategoryCodexGemma
Metaresearch0.0010.001
Meta-epidemiology (narrow)0.0000.000
Meta-epidemiology (broad)0.0000.000
Bibliometrics0.0000.001
Science and technology studies0.0000.001
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 head0.004
GPT teacher head0.280
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