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Record W2767533877 · doi:10.1097/mat.0000000000000726

Use of Remote Pulmonary Artery Pressure Monitoring (CardioMEMS System) in Total Artificial Heart to Assess Pulmonary Hemodynamics for Heart Transplantation

2017· article· en· W2767533877 on OpenAlexaboutno aff
Salman Gohar, Ziad Taimeh, Jeffrey A. Morgan, O.H. Frazier, Francisco A. Arabía, Andrew B. Civitello, Ajith Nair

Bibliographic record

VenueASAIO Journal · 2017
Typearticle
Languageen
FieldMedicine
TopicTransplantation: Methods and Outcomes
Canadian institutionsnot available
FundersSt. Jude MedicalTexas Heart Institute
KeywordsPulmonary arteryMedicineCardiologyPulmonary hypertensionInternal medicineHeart failureHeart transplantationTransplantationVascular resistanceHemodynamics

Abstract

fetched live from OpenAlex

The temporary total artificial heart (TAH-t) has been valuable as a bridge to transplantation in patients with biventricular failure. However, the challenges of accurately assessing pulmonary vascular resistance after TAH-t implantation can preclude these patients from heart transplantation, especially those with pre-existing pulmonary hypertension. The CardioMEMS Heart Failure System (St. Jude's Medical, Little Canada, MN) comprises a wireless pressure sensor that is implanted percutaneously in the pulmonary artery and transmits real-time measurements of pulmonary artery pressures. Systolic and diastolic pulmonary artery (PA) pressures measurements have been well correlated between the CardioMEMS PA Sensor and traditional Swan-Ganz catheter and between the CardioMEMS PA Sensor and standard echocardiography. Here, we report the use of the CardioMEMS device in a patient with severe pulmonary hypertension supported with a SynCardia TAH-t (Tucson, AZ) during assessment for candidacy for transplantation.

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: Observational · Consensus signal: Observational
GenreCandidate signal: Empirical · Consensus signal: Empirical
Teacher disagreement score0.001
Threshold uncertainty score0.003

Distilled classifier scores by category (both heads)

CategoryCodexGemma
Metaresearch0.0010.001
Meta-epidemiology (narrow)0.0000.000
Meta-epidemiology (broad)0.0010.000
Bibliometrics0.0010.000
Science and technology studies0.0010.001
Scholarly communication0.0010.000
Open science0.0000.001
Research integrity0.0010.001
Insufficient payload (model declined to judge)0.0010.000

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 head0.103
GPT teacher head0.366
Teacher spread0.263 · 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 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

Citations12
Published2017
Admission routes1
Has abstractyes

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