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Record W4309166485 · doi:10.1111/petr.14391

Clinical approach to mechanical circulatory support in the transplant patient from the Pediatric Heart Transplant Society

2022· article· en· W4309166485 on OpenAlexaff
David W. Bearl, Aamir Jeewa, Scott R. Auerbach, Estela Azeka, Christina Phelps, Loren D. Sacks, David N. Rosenthal, Jennifer Conway

Bibliographic record

VenuePediatric Transplantation · 2022
Typearticle
Languageen
FieldEngineering
TopicMechanical Circulatory Support Devices
Canadian institutionsUniversity of AlbertaStollery Children's HospitalHospital for Sick Children
Fundersnot available
KeywordsMedicineExtracorporeal membrane oxygenationCirculatory systemDecompensationHeart transplantationIntensive care medicineTransplantationHeart failureVentricular assist deviceSurgeryCardiology

Abstract

fetched live from OpenAlex

The use of mechanical circulatory support (MCS) for pediatric patients who have undergone heart transplant has grown rapidly in the past decade. This includes support in the immediate post-transplant period and "rescue" therapy for patient later in their transplant course. Extracorporeal membrane oxygenation (ECMO) remains a standard modality of support for intraoperative concerns and for acute decompensation in the immediate post-transplant period. However, both pulsatile and continuous flow ventricular assist devices (VADs) have been used with increasing success in transplant patients for longer durations of support. Centers participating in the Pediatric Heart Transplant Society (PHTS) were queried to provide their internal protocols and rationale for mechanical circulatory support following heart transplant. These protocols coupled with evidence-based literature were used to provide the following description of clinical approaches to MCS in the transplant patient highlighting areas of both broad consensus and significant practice variation.

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)0.003
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: Methods · Consensus signal: none
Teacher disagreement score0.003
Threshold uncertainty score0.009

Distilled classifier scores by category (both heads)

CategoryCodexGemma
Metaresearch0.0010.003
Meta-epidemiology (narrow)0.0000.000
Meta-epidemiology (broad)0.0000.000
Bibliometrics0.0010.001
Science and technology studies0.0010.001
Scholarly communication0.0010.001
Open science0.0000.001
Research integrity0.0010.002
Insufficient payload (model declined to judge)0.0030.000

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 head0.023
GPT teacher head0.238
Teacher spread0.215 · 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
GenreMethods

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
Published2022
Admission routes1
Has abstractyes

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