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Record W3037831722 · doi:10.7759/cureus.8887

Chest Pain on the Ward: A Simulation Scenario for Rural Family and Emergency Medicine Trainees

2020· article· en· W3037831722 on OpenAlexaff
Kerry-Lynn Williams, Paul R. Crocker, Adam Dubrowski

Bibliographic record

VenueCureus · 2020
Typearticle
Languageen
FieldMedicine
TopicSimulation-Based Education in Healthcare
Canadian institutionsUniversity of Ontario Institute of TechnologyMemorial University of Newfoundland
Fundersnot available
KeywordsMedicineAcute coronary syndromeCardiogenic shockChest painPresentation (obstetrics)Medical emergencyEmergency medicineShock (circulatory)ComplicationIntensive care medicineMyocardial infarctionSurgeryInternal medicine

Abstract

fetched live from OpenAlex

Chest pain is a frequently encountered emergency room presentation, of which about 15% of cases are due to acute coronary syndromes. Cardiogenic shock is a relatively uncommon complication with associated high morbidity and mortality. Emergency medicine practitioners frequently encounter critically ill patients that require quick, definitive treatment to optimize patient outcomes. These high acuity presentations often are of relatively low occurrence which makes training residents and learners challenging. Simulation-based medical education has been shown to enhance patient outcomes by teaching these high acuity low occurrence (HALO) presentations in a safe environment. Herein we describe a simulation scenario of a patient with cardiogenic shock secondary to acute coronary syndrome. It consists of a step-wise, detailed summary of the case, along with modifiers to adjust the case for repeated use, learning objectives, and a suggested evaluation.

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: Simulation or modeling · Consensus signal: none
GenreCandidate signal: Empirical · Consensus signal: Empirical
Teacher disagreement score0.005
Threshold uncertainty score0.016

Distilled classifier scores by category (both heads)

CategoryCodexGemma
Metaresearch0.0010.003
Meta-epidemiology (narrow)0.0010.000
Meta-epidemiology (broad)0.0000.001
Bibliometrics0.0000.000
Science and technology studies0.0010.001
Scholarly communication0.0010.001
Open science0.0010.002
Research integrity0.0030.002
Insufficient payload (model declined to judge)0.0050.001

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 head0.153
GPT teacher head0.389
Teacher spread0.236 · 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 designSimulation or modeling
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
Published2020
Admission routes1
Has abstractyes

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