MétaCan
Menu
Back to cohort
Record W4386070824 · doi:10.11159/icbes23.156

Computational Fluid Dynamics Analysis of Blood Flow in CerebralMycotic Aneurysms

2023· article· en· W4386070824 on OpenAlexvenueno aff
S. A. Syed Asif, B. J. Sudhir, B. S. V. Patnaik, Ram Kishan Nekkanti, Ganesh Divakar, Krishnakumar Kesavapisharady, Sam Scaria

Bibliographic record

VenueProceedings of the World Congress on Electrical Engineering and Computer Systems and Science · 2023
Typearticle
Languageen
FieldMedicine
TopicIntracranial Aneurysms: Treatment and Complications
Canadian institutionsnot available
FundersScience and Engineering Research Board
KeywordsBlood flowComputer scienceComputational fluid dynamicsCerebral blood flowDynamics (music)Fluid dynamicsMedicineRadiologyMechanicsCardiologyPhysicsAcoustics

Abstract

fetched live from OpenAlex

Mycotic aneurysm is a serious medical condition that, if not treated promptly, can lead to life-threatening complications.The geometry of a cerebral mycotic aneurysm (CMA) is extremely intricate, and each anatomy is unique.Clinical imaging depicting the complicated architecture of the CMA include Digital Subtraction Angiography (DSA) and Computed Tomography Angiogram (CTA).Analysing the hemodynamics inside the complex aneurysm is still challenging.Computational fluid dynamics (CFD) simulation of blood flow in mycotic aneurysms has emerged as a promising method for understanding the hemodynamic parameters that contribute to the formation and propagation of these aneurysms.Calculations based on computational fluid dynamics (CFD) are used to explore the hemodynamic influences on flow characteristics, secondary flow patterns, and helicity in complex aneurysms.Aneurysm initiation and growth have been widely connected to hemodynamics-based wall shear stress (WSS).The relationship between the hemodynamics of aneurysm initiation and growth that leads to rupture is not entirely established in the context of patient-specific aneurysms.CFD-based studies in CMA are scarce, and the current work fills the gap by employing a patient-specific model.

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)0.000
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: Simulation or modeling
GenreCandidate signal: Empirical · Consensus signal: Empirical
Teacher disagreement score0.444
Threshold uncertainty score0.271

Codex and Gemma teacher scores by category

CategoryCodexGemma
Metaresearch0.0000.000
Meta-epidemiology (narrow)0.0000.000
Meta-epidemiology (broad)0.0000.000
Bibliometrics0.0010.004
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 head0.007
GPT teacher head0.216
Teacher spread0.210 · 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

Citations0
Published2023
Admission routes1
Has abstractyes

Explore more

Same venueProceedings of the World Congress on Electrical Engineering and Computer Systems and ScienceSame topicIntracranial Aneurysms: Treatment and ComplicationsFrench-language works237,207