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Record W2054385588 · doi:10.1109/acc.2010.5530827

Baroreflex modeling in the genesis of stress reactivity using sigmoidal characteristic

2010· article· en· W2054385588 on OpenAlexaff
Pedram Ataee, Guy A. Dumont, W. Thomas Boyce

Bibliographic record

Venuenot available
Typearticle
Languageen
FieldMedicine
TopicHeart Rate Variability and Autonomic Control
Canadian institutionsUniversity of British Columbia
Fundersnot available
KeywordsBaroreceptorBaroreflexAutonomic nervous systemBlood pressureHeart rateReflexControl theory (sociology)NeuroscienceCardiologyComputer scienceInternal medicineMedicinePsychologyControl (management)

Abstract

fetched live from OpenAlex

According to a physiological hypothesis, children are separated into the two groups, (1)non-reactive and (2)high-reactive based on their different autonomic reactivity characteristic. In the non-reactive group, blood pressure(BP) and heart rate(HR) are regulated in a timely manner following external disturbances such as a stressful condition. However, this regulation process does not operate properly, or may even behave in an opposite direction for at least a period of the process in the high-reactive group. The purpose of this research is to analyze and compare the behavioral differences of the autonomic reactivity characteristic, represented by the short-term blood pressure regulation system (STBPRS), between these two groups of individuals. Similar to any regulation system, each component of this system has a specific role. For example, the autonomic nervous system (ANS) can be considered a controller while the heart and vasculature can be considered a plant under control. The arterial baroreceptor nerves - fiber endings in the arterial walls - play the role of sensor and feedback path. The STBPRS is called as baroreflex or baroreceptor reflex including the ANS and baroreceptors. We applied the Windkessel model and sigmoidal function as the model structures of the vasculature and baroreflex, respectively. To obtain the most similar simulated HR in comparison with measured HR, an optimization problem was defined. Due to the non-convex nature of the optimization problem, a genetic algorithm (GA) was applied to identify all of the corresponding unknown parameters for each component of the system. The obtained results of the system identification problem, verify the mentioned physiological hypothesis. Moreover, these results lead to a better understanding of the deficient baroreflex in high-reactive children. Furthermore, necessities of invasive blood pressure measurement in baroreflex studies is eliminated by using our proposed method.

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)0.001
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 score0.012
Threshold uncertainty score0.025

Distilled classifier scores by category (both heads)

CategoryCodexGemma
Metaresearch0.0010.001
Meta-epidemiology (narrow)0.0010.001
Meta-epidemiology (broad)0.0010.001
Bibliometrics0.0000.000
Science and technology studies0.0000.000
Scholarly communication0.0010.001
Open science0.0010.000
Research integrity0.0010.001
Insufficient payload (model declined to judge)0.0020.000

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 head0.052
GPT teacher head0.303
Teacher spread0.251 · 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

Citations1
Published2010
Admission routes1
Has abstractyes

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