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Record W4291001403 · doi:10.1097/mot.0000000000001012

Durable mechanical circulatory support as bridge to heart transplantation

2022· review· en· W4291001403 on OpenAlexaff
Mitesh Badiwala, Nazari Dvirnik, Vivek Rao

Bibliographic record

VenueCurrent Opinion in Organ Transplantation · 2022
Typereview
Languageen
FieldEngineering
TopicMechanical Circulatory Support Devices
Canadian institutionsToronto General HospitalUniversity Health Network
Fundersnot available
KeywordsHeart transplantationMedicineUnited Network for Organ SharingVentricular assist deviceBridge to transplantationCirculatory systemTransplantationIntensive care medicineHeart failureDestination therapyPulsatile flowArtificial heartCardiologySurgery

Abstract

fetched live from OpenAlex

PURPOSE OF REVIEW: Durable mechanical circulatory support (MCS) technology has changed over time as devices have evolved from pulsatile to continuous flow support. In this review, we discuss recent data and substantial changes to current practice as it pertains to the subject of current era durable left ventricular assist devices (LVADs) as a bridge to heart transplantation. RECENT FINDINGS: The results of heart transplantation in patients bridged with durable LVAD support are satisfactory even after prolonged duration of support. Reports of recent experience with LVAD related infection suggest that this complication has limited impact on post-transplant outcomes. Important sex-related disparities continue to exist following durable LVAD implantation. Recent changes in the United Network for Organ Sharing donor heart allocation policy have resulted in a drastic decline in the use of durable LVAD support for Bridge to Transplant in the United States. SUMMARY: Durable MCS in the form of LVAD as a BTT strategy continues to evolve over time. Optimization of its role in the treatment of end-stage heart failure, particularly in females will need to be the focus of future research.

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)0.002
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 score0.004
Threshold uncertainty score0.015

Distilled classifier scores by category (both heads)

CategoryCodexGemma
Metaresearch0.0010.002
Meta-epidemiology (narrow)0.0010.000
Meta-epidemiology (broad)0.0010.001
Bibliometrics0.0020.001
Science and technology studies0.0000.000
Scholarly communication0.0010.001
Open science0.0010.001
Research integrity0.0010.001
Insufficient payload (model declined to judge)0.0040.001

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 head0.088
GPT teacher head0.348
Teacher spread0.259 · 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

Citations6
Published2022
Admission routes1
Has abstractyes

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