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Record W1994710476 · doi:10.1109/embc.2012.6346795

Myocardial contractility: A seismocardiography approach

2012· article· en· W1994710476 on OpenAlexaff
Kouhyar Tavakolian, G. Portacio, N. R. Tamddondoust, G. Jahns, B. Ngai, Guy A. Dumont, Andrew P. Blaber

Bibliographic record

Venuenot available
Typearticle
Languageen
FieldEngineering
TopicNon-Invasive Vital Sign Monitoring
Canadian institutionsSimon Fraser UniversityUniversity of British Columbia
Fundersnot available
KeywordsContractilityStroke volumeVolume (thermodynamics)Derivative (finance)CardiologyMedicineInternal medicineBlood pressureMathematicsHeart ratePhysicsThermodynamics

Abstract

fetched live from OpenAlex

Features are extracted from seismocardiogram data to correlate with two indexes of myocardial contractility: dP/dt(max) (maximum first derivative of left ventricular pressure) and stroke volume. In the first study on three pigs, it is shown that the time period between the R peak of the ECG and the first peak of the SCG (R-AO period or pre-ejection period, PEP) correlated (r= -0.86) with dP/dt(max). In the second study, stroke volume is gradually reduced in five human subjects using lower body negative pressure. The same feature as the pigs (R-AO) is correlated the most with stroke volume (r= -0.90).

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: Bench or experimental · Consensus signal: none
GenreCandidate signal: Empirical · Consensus signal: none
Teacher disagreement score0.002
Threshold uncertainty score0.004

Distilled classifier scores by category (both heads)

CategoryCodexGemma
Metaresearch0.0000.001
Meta-epidemiology (narrow)0.0010.000
Meta-epidemiology (broad)0.0000.000
Bibliometrics0.0020.001
Science and technology studies0.0000.000
Scholarly communication0.0010.000
Open science0.0000.000
Research integrity0.0010.000
Insufficient payload (model declined to judge)0.0010.001

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 head0.012
GPT teacher head0.201
Teacher spread0.189 · 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 designBench or experimental
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

Citations47
Published2012
Admission routes1
Has abstractyes

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