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Record W2910367582 · doi:10.1097/hco.0000000000000600

Mechanical circulatory support in the heart failure population

2019· review· en· W2910367582 on OpenAlexaff
Hamed Nazzari, Colin D. Chue, Mustafa Toma

Bibliographic record

VenueCurrent Opinion in Cardiology · 2019
Typereview
Languageen
FieldEngineering
TopicMechanical Circulatory Support Devices
Canadian institutionsSt. Paul's HospitalUniversity of British Columbia
Fundersnot available
KeywordsMedicineDestination therapyIntensive care medicineHeart failureAdverse effectVentricular assist deviceHeart transplantationStroke (engine)TransplantationPopulationThrombosisQuality of life (healthcare)CardiologySurgeryInternal medicine

Abstract

fetched live from OpenAlex

PURPOSE OF REVIEW: Use of durable left ventricular assist devices (LVADs) has increased considerably in recent years because of the insufficient supply of donor hearts for cardiac transplantation and improvement in outcomes from refinements in technology. This review examines clinical utility of these devices and summarizes the most recent evidence supporting the use of LVAD therapy. RECENT FINDINGS: There continues to be significant advancements made in LVAD technology, which has resulted in improvements in the rates of adverse events and overall patient quality of life. Specifically, less invasive and improved surgical techniques have resulted in fewer incidence of pump thrombosis and stringent blood pressure management have been shown to significantly decrease stroke rates. SUMMARY: The continued advances in LVAD therapy have resulted in significant improvement in overall survival; however, complication rates remain relatively high. Future work will focus on improvements in adverse outcomes and ultimately the possibility that LVADs will be a viable alternative to transplantation in patients with end-stage heart failure.

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 score0.005
Threshold uncertainty score0.015

Distilled classifier scores by category (both heads)

CategoryCodexGemma
Metaresearch0.0010.002
Meta-epidemiology (narrow)0.0010.000
Meta-epidemiology (broad)0.0020.001
Bibliometrics0.0020.002
Science and technology studies0.0000.000
Scholarly communication0.0010.001
Open science0.0010.001
Research integrity0.0010.001
Insufficient payload (model declined to judge)0.0050.001

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 head0.110
GPT teacher head0.363
Teacher spread0.253 · 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

Citations5
Published2019
Admission routes1
Has abstractyes

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