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Record W4407352613 · doi:10.1016/j.cjca.2025.02.006

The Etiology and Management of Critical Acute Right Heart Failure

2025· review· en· W4407352613 on OpenAlexafffundvenue
Adriana Luk, Ricardo Teijeiro‐Paradis, Andrew Kochan, Filio Billia, Ghislaine Douflé, Sheldon Magder, Asher A. Mendelson, Caroline McGuinty, John Granton

Bibliographic record

VenueCanadian Journal of Cardiology · 2025
Typereview
Languageen
FieldEngineering
TopicMechanical Circulatory Support Devices
Canadian institutionsUniversity of OttawaUniversity of ManitobaMcGill University Health CentreUniversity of TorontoUniversity Health Network
FundersManitoba Medical Service Foundation
KeywordsMedicineEtiologyHeart failureIntensive care medicineRight heart failureCardiologyManagement of heart failureInternal medicine

Abstract

fetched live from OpenAlex

Right ventricular failure contributes to the morbidity and mortality of acute myocardial function, massive pulmonary embolism, and chronic pulmonary hypertension. Understanding how the normal physiology of the right ventricle (RV) is disrupted is integral to managing patients who present with RV decompensation. Therapeutic advances in mechanical circulatory support, pharmacotherapies to reduce afterload, mechanical and chemical lytic therapies for acute pulmonary embolism have improved outcomes of patients by offloading the RV. In this report we provide an overview of the physiology of the RV, medical management (volume optimization, hemodynamic targets, rhythm management), along with critical care-specific topics (induction with mechanical ventilation, sedation strategies, and mechanical circulatory support) and provide a framework for managing patients who present with leveraging principles of preload, contractility, and afterload. Last, because of the complexity of right ventricular failure management, and the complexity of presentation, we also discuss the role of team-based approach (cardiogenic shock and pulmonary embolism response teams), and highlight its benefits at improving outcomes.

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 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 score0.796
Threshold uncertainty score0.588

Codex and Gemma teacher scores by category

CategoryCodexGemma
Metaresearch0.0000.000
Meta-epidemiology (narrow)0.0000.000
Meta-epidemiology (broad)0.0020.000
Bibliometrics0.0000.000
Science and technology studies0.0000.000
Scholarly communication0.0000.000
Open science0.0000.000
Research integrity0.0000.001
Insufficient payload (model declined to judge)0.0000.000

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 head0.014
GPT teacher head0.272
Teacher spread0.258 · 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.

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

Citations7
Published2025
Admission routes3
Has abstractyes

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