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Record W2086023854 · doi:10.1164/rccm.201104-0662ci

Intensive Care Unit Management of Patients with Severe Pulmonary Hypertension and Right Heart Failure

2011· review· en· W2086023854 on OpenAlexaff
Marius M. Hoeper, John Granton

Bibliographic record

VenueAmerican Journal of Respiratory and Critical Care Medicine · 2011
Typereview
Languageen
FieldMedicine
TopicPulmonary Hypertension Research and Treatments
Canadian institutionsUniversity of Toronto
Fundersnot available
KeywordsMedicineIntensive care unitPulmonary hypertensionHeart failureRight heart failureIntensive care medicineIntensive careCardiologyRight heartInternal medicine

Abstract

fetched live from OpenAlex

Despite advances in medical therapies, pulmonary arterial hypertension (PAH) continues to cause significant morbidity and mortality. Although the right ventricle (RV) can adapt to an increase in afterload, progression of the pulmonary vasculopathy that characterizes PAH causes many patients to develop progressive right ventricular failure. Furthermore, acute right ventricular decompensation may develop from disorders that lead to either an acute increase in cardiac demand, such as sepsis, or to an increase in ventricular afterload, including interruptions in medical therapy, arrhythmia, or pulmonary embolism. The poor reserve of the right ventricle, RV ischemia, and adverse right ventricular influence on left ventricular filling may lead to a global reduction in oxygen delivery and multiorgan failure. There is a paucity of data to guide clinicians caring for acute right heart failure in PAH. Treatment recommendations are frequently based on animal models of acute right heart failure or case series in humans with other causes of pulmonary hypertension. Successful treatment often requires that invasive hemodynamics be used to monitor the effect of strategies that are based primarily on biological plausibility. Herein we have developed an approach based on the current understanding of RV failure in PAH and have attempted to develop a treatment paradigm based on physiological principles and available evidence.

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: Other design · Consensus signal: none
GenreCandidate signal: Review · Consensus signal: Review
Teacher disagreement score0.943
Threshold uncertainty score0.992

Codex and Gemma teacher scores by category

CategoryCodexGemma
Metaresearch0.0000.000
Meta-epidemiology (narrow)0.0000.000
Meta-epidemiology (broad)0.0030.000
Bibliometrics0.0000.000
Science and technology studies0.0000.002
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 head0.039
GPT teacher head0.328
Teacher spread0.289 · 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 designOther design
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

Citations309
Published2011
Admission routes1
Has abstractyes

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