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Record W2020168461 · doi:10.5539/apr.v5n3p19

Mathematical Model of Arterial Hemodynamics, Description, Computer Implementation, Results Comparison

2013· article· en· W2020168461 on OpenAlexvenueno aff
Elshin Mikhail Anatolievich, Guljaev Juriy Petrovich

Bibliographic record

VenueApplied Physics Research · 2013
Typearticle
Languageen
FieldMedicine
TopicCerebrovascular and Carotid Artery Diseases
Canadian institutionsnot available
Fundersnot available
KeywordsAdinaComputer scienceSoftwareMathematical modelBasis (linear algebra)Finite element methodSimulationMathematicsProgramming languageStatisticsPhysics

Abstract

fetched live from OpenAlex

Analysis of basic parameters of a blood pressure in major arteries of human body is essential issue nowadays. It can help to select more appropriate way of surgical intervention for people who are ill with pathological changes of arteries, for example atherosclerosis. New mathematical model of blood pressure was developed to do such kind of analysis. The mathematical model is one-dimensional in terms of spatial coordinates. It contains verity of parameters that makes available to adjust this model to arterial bed of any exact case of any exact patient. Piece of software was implemented on the basis of the mathematical model. The software is aimed to build any particular part of arterial system and calculate parameters of blood pressure in it. Comparison of results gathered by developed software and by finite-element analysis software ADINA showed a slight difference. But the calculation time for the first one is a considerably less.

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 machine prediction

Teacher imitation

Not calibrated prevalence, not ground truth. Human validation pending. The Gemma side is a direct model label for every work in the frame, read from the title-only record. The Codex side is a classifier learned from the 10,348 direct Codex labels and calibrated to design-weighted sample rates; fields without enough sample support carry no Codex call. Candidate is the union of the two sides; consensus is their intersection. These outputs are machine_predicted_unvalidated and are not human labels.

metaresearch head score (Codex)0.001
metaresearch head score (Gemma)0.002
Version: metacan-v3-hybrid-931329e0061cValidation 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: none
Teacher disagreement score0.005
Threshold uncertainty score0.016

Distilled classifier scores by category (both heads)

CategoryCodexGemma
Metaresearch0.0010.002
Meta-epidemiology (narrow)0.0000.000
Meta-epidemiology (broad)0.0000.001
Bibliometrics0.0010.001
Science and technology studies0.0000.000
Scholarly communication0.0010.001
Open science0.0010.000
Research integrity0.0010.001
Insufficient payload (model declined to judge)0.0050.001

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 head0.085
GPT teacher head0.374
Teacher spread0.289 · 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 source (direct Gemma or distilled Codex), 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
Published2013
Admission routes1
Has abstractyes

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