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Record W4390406358 · doi:10.1177/02676591231225717

Designing and maintaining a rescue extracorporeal life support program: A holistic simulation approach

2023· article· en· W4390406358 on OpenAlexafffund
Tanya Spence, Dejana Nikitović, Donovan Duncan, Steve Menzies, Anna Zadunayski, Mary Brindle, Jaime Blackwood

Bibliographic record

VenuePerfusion · 2023
Typearticle
Languageen
FieldMedicine
TopicSimulation-Based Education in Healthcare
Canadian institutionsFoothills Medical CentreAlberta Children's HospitalUniversity of CalgaryAlberta Health Services
FundersAlberta Children's Hospital FoundationExtracorporeal Life Support OrganizationChildren's Hospital Foundation
KeywordsMedicineMultidisciplinary approachWorkflowLife supportExtracorporeal membrane oxygenationMultidisciplinary teamMedical emergencyIntensive care medicineNursingComputer scienceSurgery

Abstract

fetched live from OpenAlex

Rescue Extracorporeal Life Support Programs based at non-cardiac surgery centers have unique needs to be able to ensure successful outcomes despite low patient volumes. In this paper we describe the important role simulation had in each stage of development, implementation, and maintenance of our pediatric Rescue ECLS Program. Systems-focused simulations were used to develop robust workflows, processes, and bundles. Simulation-based education targeted the acquisition and maintenance of clinical skills for individual team members, bringing together a multidisciplinary team of local clinicians who do not routinely perform pediatric cannulation related tasks. Translational simulation ensured continued improvement by addressing adverse events or latent safety threats observed during system-focused or educational simulations. Realism of all simulations was our priority, and was achieved through in situ simulations, participation of multidisciplinary teams, use of real equipment and medical supplies, and use of a high-fidelity cannulation manikin. This holistic simulation approach allowed us to overcome the barriers to high quality care, and maintain outcomes comparable to high volume centers. A similar approach can help other centers design simulation for their own Rescue ECLS Program, and can be translated to other high-risk and high-acuity critical care programs.

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)0.001
metaresearch head score (Gemma)0.001
Version: codex-gemma-dda1882f352aValidation status: machine_predicted_unvalidated
Candidate categoriesnone
Consensus categoriesnone
DomainCandidate signal: none · Consensus signal: none
Study designCandidate signal: Observational · Consensus signal: none
GenreCandidate signal: Empirical · Consensus signal: Empirical
Teacher disagreement score0.578
Threshold uncertainty score0.576

Codex and Gemma teacher scores by category

CategoryCodexGemma
Metaresearch0.0010.001
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 integrity0.0000.000
Insufficient payload (model declined to judge)0.0000.000

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 head0.131
GPT teacher head0.412
Teacher spread0.282 · 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

Citations0
Published2023
Admission routes2
Has abstractyes

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