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Record W3207482935 · doi:10.7759/cureus.18824

The Development of a Cost-Effective Infant Intraosseous Infusion Simulator for Neonatal Resuscitation Program Training

2021· article· en· W3207482935 on OpenAlexafffund
Julia Micallef, Artur Arutiunian, Jennifer Hiley, Andy Benson, Adam Dubrowski

Bibliographic record

VenueCureus · 2021
Typearticle
Languageen
FieldMedicine
TopicSimulation-Based Education in Healthcare
Canadian institutionsLakeridge HealthOntario Tech University
FundersMitacs
KeywordsMedicineSimulationTraining (meteorology)ResuscitationProcess (computing)Medical physicsComputer scienceSurgeryOperating system

Abstract

fetched live from OpenAlex

Simulation-based medical education (SBME) employs realistic simulators to allow physicians and medical students to learn and practice high acuity, low occurrence (HALO) skills such as the intraosseous (IO) infusion. Previous research was done to develop and evaluate a three-dimensional (3D)-printed adult proximal tibia IO simulator and was rated as a valuable and realistic medical education training tool. This report focuses on implementing this IO simulator for neonatal resuscitation program (NRP) training purposes, as well as to explain the process of redeveloping the previous adult IO simulator and the development of a stand, called the maxSIMbox, to hold the simulators, as well as the tools needed to perform an IO infusion. The feedback provided from stakeholders was helpful, with an emphasis on providing stability to both the infant IO simulator and the maxSIMbox. From this feedback, a functional and cost-effective simulator was developed to practice this HALO skill and is currently being used for NRP training.

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)0.000
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: Other design · Consensus signal: none
GenreCandidate signal: Empirical · Consensus signal: Empirical
Teacher disagreement score0.923
Threshold uncertainty score0.327

Codex and Gemma teacher scores by category

CategoryCodexGemma
Metaresearch0.0000.001
Meta-epidemiology (narrow)0.0000.000
Meta-epidemiology (broad)0.0000.000
Bibliometrics0.0000.000
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 head0.062
GPT teacher head0.408
Teacher spread0.347 · 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 designOther design
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

Citations6
Published2021
Admission routes2
Has abstractyes

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