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Record W1597212461 · doi:10.4137/cmc.s18740

End-systolic Pressure–Volume Relation, Ejection Fraction, and Heart Failure: Theoretical Aspect and Clinical Applications

2015· article· en· W1597212461 on OpenAlexaff
Rachad M. Shoucri

Bibliographic record

VenueClinical Medicine Insights Cardiology · 2015
Typearticle
Languageen
FieldMedicine
TopicCardiovascular Function and Risk Factors
Canadian institutionsRoyal Military College of Canada
Fundersnot available
KeywordsCardiologyEjection fractionInternal medicineVentricleMathematicsHeart failureMedicine

Abstract

fetched live from OpenAlex

A mathematical formalism describing the nonlinear end-systolic pressure-volume relation (ESPVR) is used to derive new indexes that can be used to assess the performance of the heart left ventricle by using the areas under the ESPVR (units of energy), the ordinates of the ESPVR (units of pressure), or from slopes of the curvilinear ESPVR. New relations between the ejection fraction (EF) and the parameters describing the ESPVR give some insight into the problem of heart failure (HF) with normal or preserved ejection fraction. Relations between percentage occurrence of HF and indexes derived from the ESPVR are also discussed. When ratios of pressures are used, calculation can be done in a noninvasive way with the possibility of interesting applications in routine clinical work. Applications to five groups of clinical data are given and discussed (normal group, aortic stenosis, aortic valvular regurgitation, mitral valvular regurgitation, miscellaneous cardiomyopathies). No one index allows a perfect segregation between all clinical groups, it is shown that appropriate use of two indexes (bivariate analysis) can lead to better separation of different clinical groups.

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)0.003
metaresearch head score (Gemma)0.004
Version: codex-gemma-dda1882f352aValidation status: machine_predicted_unvalidated
Candidate categoriesScience and technology studies
Consensus categoriesnone
DomainCandidate signal: none · Consensus signal: none
Study designCandidate signal: Observational · Consensus signal: none
GenreCandidate signal: Empirical · Consensus signal: Empirical
Teacher disagreement score0.484
Threshold uncertainty score1.000

Codex and Gemma teacher scores by category

CategoryCodexGemma
Metaresearch0.0030.004
Meta-epidemiology (narrow)0.0000.000
Meta-epidemiology (broad)0.0020.000
Bibliometrics0.0000.000
Science and technology studies0.0000.003
Scholarly communication0.0000.000
Open science0.0000.000
Research integrity0.0010.001
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 head0.040
GPT teacher head0.349
Teacher spread0.310 · 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.

Study designObservational
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

Citations3
Published2015
Admission routes1
Has abstractyes

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