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Record W3193658650 · doi:10.1016/j.xjon.2021.08.005

Rates and types of infections in left ventricular assist device recipients: A scoping review

2021· review· en· W3193658650 on OpenAlexaboutno aff
Michael Pienta, Supriya Shore, Francis D. Pagani, Donald S. Likosky, Ashraf Shaaban, Abdel Aziz Abou El Ela, Paul C. Tang, Michael P. Thompson, Keith D. Aaronson, Thomas Cascino, Katherine B. Salciccioli, Min Zhang, Jeffrey S. McCullough, Michelle Hou, Allison M. Janda, Michael R. Mathis, Tessa M.F. Watt, Michael J. Pienta, Alexander A. Brescia, Austin E. Airhart, Daniel Liesman, Khalil Nassar

Bibliographic record

VenueJTCVS Open · 2021
Typereview
Languageen
FieldEngineering
TopicMechanical Circulatory Support Devices
Canadian institutionsnot available
FundersNational Heart, Lung, and Blood InstituteAgency for Healthcare Research and QualityNational Institutes of Health
KeywordsMedicineVentricular assist deviceIntensive care medicineHeart failureInternal medicine

Abstract

fetched live from OpenAlex

Use of left ventricular assist devices (LVADs) has increased over time as the number of patients with end-stage heart failure increases relative to the availability of heart transplant donor organs. Although outcomes in LVAD recipients have improved with advances in technology, infections remain a persistent problem and are the most common adverse event after LVAD implantation.1 Infections associated with LVAD implantation remain a persistent problem and are the most common adverse event during the first year after LVAD implantation, with only 59% of patients free from major infection at 1 year after implant.

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)0.018
metaresearch head score (Gemma)0.097
Version: metacan-v3-hybrid-931329e0061cValidation status: machine_predicted_unvalidated
Candidate categoriesnone
Consensus categoriesnone
DomainCandidate signal: none · Consensus signal: none
Study designCandidate signal: Systematic review · Consensus signal: Systematic review
GenreCandidate signal: Review · Consensus signal: Review
Teacher disagreement score0.023
Threshold uncertainty score0.096

Distilled classifier scores by category (both heads)

CategoryCodexGemma
Metaresearch0.0180.097
Meta-epidemiology (narrow)0.0020.001
Meta-epidemiology (broad)0.0040.008
Bibliometrics0.0230.020
Science and technology studies0.0010.001
Scholarly communication0.0050.005
Open science0.0020.003
Research integrity0.0030.002
Insufficient payload (model declined to judge)0.0060.001

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 head0.070
GPT teacher head0.379
Teacher spread0.309 · 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 designSystematic review
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

Citations19
Published2021
Admission routes1
Has abstractyes

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