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Record W2028880633 · doi:10.1109/embc.2012.6345910

Model-based estimation of blood pressure response to epinephrine

2012· article· en· W2028880633 on OpenAlexaff
Ramin Bighamian, Andrew Reisner, Jin‐Oh Hahn

Bibliographic record

Venuenot available
Typearticle
Languageen
FieldMedicine
TopicHeart Rate Variability and Autonomic Control
Canadian institutionsUniversity of Alberta
Fundersnot available
KeywordsEpinephrineBlood pressureMedicineHeart rateHemodynamicsPeripheral resistanceDiastoleMean arterial pressureStroke volumeMathematicsCardiologyInternal medicineAnesthesia

Abstract

fetched live from OpenAlex

In this study, we present a model-based approach to estimation of blood pressure (BP) response to epinephrine. The proposed approach estimates systolic (SBP), mean (MAP) and diastolic (DBP) BP based on a 2-parameter windkessel (WK) model with dose-dependent total peripheral resistance (TPR), arterial compliance (AC) and stroke volume (SV) indices that is driven by the epinephrine dose, heart rate (HR). Using the epinephrine dose and hemodynamic response data collected for young/old normotensive and hypertensive subject groups, four group-specific models as well as a generalized model were developed and then were evaluated for BP estimation performance. The results indicated that the group-specific model is superior to its generalized counterpart; on average, the root-mean-squared SBP, MAP and DBP estimation errors associated with the group-specific model were only 34%, 52% and 69%, respectively, compared with the generalized 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 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)0.000
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 score0.005
Threshold uncertainty score0.010

Distilled classifier scores by category (both heads)

CategoryCodexGemma
Metaresearch0.0000.001
Meta-epidemiology (narrow)0.0000.000
Meta-epidemiology (broad)0.0010.001
Bibliometrics0.0000.000
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 head0.020
GPT teacher head0.284
Teacher spread0.264 · 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
Published2012
Admission routes1
Has abstractyes

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