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Review of Ambulatory Pulmonary Artery Catheterization in the Management of Advanced Heart Failure

2011· article· en· W1483708376 on OpenAlexaff
Sharayne Mark, Pedro CalderonArtero, Lisa Kakinami, Jeffrey D. Alexis, Leway Chen, Eugene Storozynsky, Michael Fong

Bibliographic record

VenueCongestive Heart Failure · 2011
Typearticle
Languageen
FieldEngineering
TopicMechanical Circulatory Support Devices
Canadian institutionsMcGill University
Fundersnot available
KeywordsMedicineAmbulatoryHeart failureInotropePulmonary wedge pressureCardiologyPulmonary arteryInternal medicineEjection fractionRetrospective cohort studyAdverse effectCardiac catheterization

Abstract

fetched live from OpenAlex

The use of pulmonary artery catheterization (PAC) has declined secondary to associated complications and lack of demonstrable efficacy in the inpatient setting. Few studies have been published on the use of PAC in nonacute heart failure (HF) patients. The purpose of this study was to review the use of PAC in guiding advanced therapy in nonacute ambulatory HF patients. A retrospective observational study assessing our group's practice pattern with regard to the use of PAC in 515 ambulatory HF patients, outcomes, and adverse events that resulted from its use was performed. A total of 159 ambulatory HF patients were referred for PAC; 7% underwent heart transplant, 6% had ventricular assist device (VAD) placement, 18% underwent inotropic therapy, and 48% had addition of therapy while 14% had subtraction of therapy. Adverse events occurred in 4% of ambulatory PAC. Patients who underwent heart transplant, VAD, or inotropic therapy had significantly elevated pulmonary capillary wedge pressures, mean pulmonary artery pressures, and depressed cardiac index. Patients selected for inotropic therapy also had significantly elevated right atrial pressures and depressed ejection fractions. PAC use safely guided medical therapy in more than half of the nonacute ambulatory patients.

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: Observational · Consensus signal: Observational
GenreCandidate signal: Empirical · Consensus signal: Empirical
Teacher disagreement score0.352
Threshold uncertainty score0.645

Codex and Gemma teacher scores by category

CategoryCodexGemma
Metaresearch0.0000.000
Meta-epidemiology (narrow)0.0000.000
Meta-epidemiology (broad)0.0000.000
Bibliometrics0.0000.000
Science and technology studies0.0000.000
Scholarly communication0.0000.000
Open science0.0000.000
Research integrity0.0000.000
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 head0.019
GPT teacher head0.231
Teacher spread0.211 · 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 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

Citations1
Published2011
Admission routes1
Has abstractyes

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