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Record W2886514200 · doi:10.1097/hco.0000000000000540

Right ventricular strain

2018· review· en· W2886514200 on OpenAlexaff
Bilal Ayach, Nowell M. Fine, Lawrence Rudski

Bibliographic record

VenueCurrent Opinion in Cardiology · 2018
Typereview
Languageen
FieldMedicine
TopicPulmonary Hypertension Research and Treatments
Canadian institutionsUniversity of CalgaryLibin Cardiovascular Institute of AlbertaMcGill UniversityJewish General Hospital
Fundersnot available
KeywordsMedicineCardiologySpeckle tracking echocardiographyInternal medicineVentricular functionPulmonary hypertensionDoppler echocardiographyDiseaseHeart failureRadiologyEjection fractionBlood pressureDiastole

Abstract

fetched live from OpenAlex

PURPOSE OF REVIEW: Right ventricular (RV) assessment has long been challenging and technically difficult using echocardiography. This is mainly the result of the asymmetrical shape of the RV making it difficult to visualize on one-or-two dedicated views, thus requiring multiple integrated views and subjective assessment. Measurement of tricuspid annular systolic plane excursion and RV tissue Doppler velocity have become relied-upon methods of objective assessments; however, have limitations for characterizing true RV physiology. RECENT FINDINGS: Studies suggest that two-dimensional RV free wall longitudinal systolic strain (RVFWS) using speckle-tracking echocardiography has emerged as a reproducible, feasible and highly prognostic technique for quantifying RV function. This has been demonstrated for patients with heart failure, ischemic heart disease, pulmonary hypertension, infiltrative disease and many other types of cardiovascular disease. SUMMARY: The current review outlines the clinical use of RVFWS, and its integration with other commonly used echocardiographic measurements to more accurately assess RV function, cause and prognosis to guide and improve patient care decision making.

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)0.000
metaresearch head score (Gemma)0.000
Version: codex-gemma-dda1882f352aValidation status: machine_predicted_unvalidated
Candidate categoriesMeta-epidemiology (narrow), Insufficient payload (model declined to judge)
Consensus categoriesnone
DomainCandidate signal: none · Consensus signal: none
Study designCandidate signal: Not applicable · Consensus signal: none
GenreCandidate signal: Review · Consensus signal: Review
Teacher disagreement score0.926
Threshold uncertainty score1.000

Codex and Gemma teacher scores by category

CategoryCodexGemma
Metaresearch0.0000.000
Meta-epidemiology (narrow)0.0000.000
Meta-epidemiology (broad)0.0020.001
Bibliometrics0.0010.000
Science and technology studies0.0000.000
Scholarly communication0.0000.000
Open science0.0000.000
Research integrity0.0000.001
Insufficient payload (model declined to judge)0.0000.001

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 head0.164
GPT teacher head0.447
Teacher spread0.283 · 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 designNot applicable
Domainnot available
GenreReview

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

Citations34
Published2018
Admission routes1
Has abstractyes

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