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Record W4313830812 · doi:10.1093/ehjacc/zuac132

Simulation in cardiac critical care

2023· article· en· W4313830812 on OpenAlexaff
Tiffany Yuen, Peter G. Brindley, Janek Senaratne

Bibliographic record

VenueEuropean Heart Journal Acute Cardiovascular Care · 2023
Typearticle
Languageen
FieldMedicine
TopicSimulation-Based Education in Healthcare
Canadian institutionsUniversity of Alberta
Fundersnot available
KeywordsIntensive careTeamworkMedicineIntensive care medicineQuality (philosophy)Computer scienceRisk analysis (engineering)Medical emergency

Abstract

fetched live from OpenAlex

Medical simulation is a broad topic but at its core is defined as any effort to realistically reproduce a clinical procedure, team, or situation. Its goal is to allow risk-free practice-until-perfect, and in doing so, augment performance, efficiency, and safety. In medicine, even complex clinical situations can be dissected into reproducible parts that may be repeated and mastered, and these iterative improvements can add up to major gains. With our modern cardiac intensive care units treating a growing number of medically complex patients, the need for well-trained personnel, streamlined care pathways, and quality teamwork is imperative for improved patient outcomes. Simulation is therefore a potentially life-saving tool relevant to anyone working in cardiac intensive care. Accordingly, we believe that simulation is a priority for cardiac intensive care, not just a luxury. We offer the following primer on simulation in the cardiac intensive care environment.

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 categoriesMeta-epidemiology (narrow)
Consensus categoriesnone
DomainCandidate signal: none · Consensus signal: none
Study designCandidate signal: Observational · Consensus signal: Observational
GenreCandidate signal: Empirical · Consensus signal: Empirical
Teacher disagreement score0.357
Threshold uncertainty score1.000

Codex and Gemma teacher scores by category

CategoryCodexGemma
Metaresearch0.0010.001
Meta-epidemiology (narrow)0.0000.000
Meta-epidemiology (broad)0.0010.001
Bibliometrics0.0010.001
Science and technology studies0.0000.000
Scholarly communication0.0000.000
Open science0.0000.000
Research integrity0.0000.001
Insufficient payload (model declined to judge)0.0000.001

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 head0.069
GPT teacher head0.391
Teacher spread0.322 · 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.

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

Citations4
Published2023
Admission routes1
Has abstractyes

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