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The Use of a Discrete Event Simulation Model to Evaluate the Impact of a Quality Improvement Initiative on Patient Flow in a Pediatric Emergency Department

2019· article· en· W3036620539 on OpenAlexaff
Kenneth McKinley, Quynh Doan, John Babineau, Cindy G. Roskind

Bibliographic record

VenuePEDIATRICS · 2019
Typearticle
Languageen
FieldHealth Professions
TopicHealthcare Operations and Scheduling Optimization
Canadian institutionsBC Children's Hospital
Fundersnot available
KeywordsMedicineEmergency departmentDiscrete event simulationPopulationEmergency medicineMedical emergencyEvent (particle physics)Protocol (science)Quality managementIntensive care medicineOperations managementSimulationNursing

Abstract

fetched live from OpenAlex

Background: Time-specific protocols created for patients presenting to Pediatric Emergency Departments (PED) likely affect the general flow of patients through those systems. One Quality Improvement (QI) protocol at our institution has successfully decreased the time to antibiotic delivery in oncology patients with a central line who present with fever, a population that represented 1.3% of Emergency Severity Index (ESI) level 2 patients in 2016. The impact on general flow of a change directed at a small patient population is extremely challenging to study in a dynamic, real-world system. Discrete Event …

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)0.002
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: Simulation or modeling · Consensus signal: Simulation or modeling
GenreCandidate signal: Empirical · Consensus signal: Empirical
Teacher disagreement score0.025
Threshold uncertainty score0.343

Codex and Gemma teacher scores by category

CategoryCodexGemma
Metaresearch0.0020.001
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 head0.175
GPT teacher head0.488
Teacher spread0.313 · 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 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

Citations0
Published2019
Admission routes1
Has abstractyes

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