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Record W4224210460 · doi:10.1097/mat.0000000000001734

Key Considerations in Establishing a Pediatric Rescue Extracorporeal Life Support Program: A Multi Methods Review

2022· article· en· W4224210460 on OpenAlexaff

Bibliographic record

VenueASAIO Journal · 2022
Typearticle
Languageen
FieldMedicine
TopicCardiac Arrest and Resuscitation
Canadian institutionsAlberta Children's HospitalUniversity of Calgary
Fundersnot available
KeywordsLife supportDebriefingExtracorporealExtracorporeal membrane oxygenationMultidisciplinary approachThematic analysisMultidisciplinary teamRescue therapyAdvanced cardiac life support

Abstract

fetched live from OpenAlex

Extracorporeal life support (ECLS) is generally limited to centers with cardiac surgery. However, pediatric centers without cardiac surgery can still provide potentially lifesaving ECLS through a Rescue Program, allowing a local team to cannulate and stabilize patients before they are transported to a center with cardiac surgery support for ongoing care. This multimethod study provides an exploration of pediatric ECLS team insights regarding program implementation and offers recommendations for other centers wishing to develop a similar program. We performed surveys and semi-structured interviews to gather perspectives from ECLS team members. Demographics and preliminary perspectives were obtained from surveys. Interviews were transcribed and coded using thematic analysis to identify key considerations, facilitators, and barriers related to rescue program implementation. Our multidisciplinary ECLS team perceived great value in the rescue program and identified elements critical to successful program development and implementation, including barriers that might exist for any center wishing to set up a similar program. Participants emphasized that the initial design and continued maintenance of any Rescue ECLS Program be a comprehensive, multidisciplinary initiative. Clear communication, a mechanism for debriefing and feedback, and a strategy allowing for flexible program evolution are essential.

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 distilled prediction

Teacher imitation

Not calibrated prevalence, not ground truth. Human validation pending. Learned from the 10,348 direct Codex labels and 10,348 direct Gemma labels. Candidate is the union of thresholded teacher heads; consensus is their intersection. These outputs are machine_predicted_unvalidated and are not human labels or direct frontier model labels.

metaresearch head score (Codex)0.004
metaresearch head score (Gemma)0.002
Version: codex-gemma-dda1882f352aValidation 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 score0.398
Threshold uncertainty score0.971

Codex and Gemma teacher scores by category

CategoryCodexGemma
Metaresearch0.0040.002
Meta-epidemiology (narrow)0.0000.000
Meta-epidemiology (broad)0.0000.000
Bibliometrics0.0000.001
Science and technology studies0.0000.000
Scholarly communication0.0000.000
Open science0.0000.000
Research integrity0.0000.001
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 head0.060
GPT teacher head0.406
Teacher spread0.346 · 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 teacher head, 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

Citations1
Published2022
Admission routes1
Has abstractyes

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