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Record W2010696669 · doi:10.1097/hco.0b013e32834fec4e

Imaging the failing right ventricle

2012· review· en· W2010696669 on OpenAlexaff
Peter R. Mitoff, Luc Beauchesne, Alexander Dick, Benjamin J.W. Chow, Rob Beanlands, Haissam Haddad, Lisa Mielniczuk

Bibliographic record

VenueCurrent Opinion in Cardiology · 2012
Typereview
Languageen
FieldMedicine
TopicPulmonary Hypertension Research and Treatments
Canadian institutionsUniversity of Ottawa
Fundersnot available
KeywordsMedicineVentricleModalitiesMagnetic resonance imagingModality (human–computer interaction)Cardiac magnetic resonanceRadiologyCardiac imagingCardiac magnetic resonance imagingRadionuclide ventriculographyCardiologyHeart failureEjection fraction

Abstract

fetched live from OpenAlex

PURPOSE OF REVIEW: This article will review the noninvasive modalities currently available for imaging the right ventricle, including two-dimensional echocardiography, cardiac magnetic resonance (CMR), multidetector computed tomography (MDCT), radionuclide ventriculography (RNV) and PET. RECENT FINDINGS: Improvements in established imaging techniques, as well as development of newer imaging modalities, have shed light on the right ventricle's adaptation to pressure and volume overload states and have allowed better prognostication in patients with right ventricular failure (RVF). SUMMARY: As therapies are developed to alter the natural history of RVF, a better understanding of the imaging modalities for the assessment of right ventricular morphology and function is needed. This review will provide an approach to investigating the patient with suspected RVF and highlight the strengths and weakness of each imaging modality.

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: Not applicable · Consensus signal: none
GenreCandidate signal: Review · Consensus signal: Review
Teacher disagreement score0.003
Threshold uncertainty score0.009

Distilled classifier scores by category (both heads)

CategoryCodexGemma
Metaresearch0.0010.001
Meta-epidemiology (narrow)0.0010.000
Meta-epidemiology (broad)0.0020.000
Bibliometrics0.0020.001
Science and technology studies0.0000.001
Scholarly communication0.0010.001
Open science0.0010.001
Research integrity0.0010.001
Insufficient payload (model declined to judge)0.0030.003

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 head0.177
GPT teacher head0.445
Teacher spread0.267 · 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 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

Citations15
Published2012
Admission routes1
Has abstractyes

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