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Record W3042029664 · doi:10.7759/cureus.9134

Automated Inflating Resuscitator (AIR): Design and Development of a 3D-Printed Ventilator Prototype and Corresponding Simulation Scenario Based on the Management of a Critical COVID-19 Patient

2020· article· en· W3042029664 on OpenAlexaff
Bruno Gino, Zhujiang Wang, Phil D'Entremont, Tia Renouf, Adam Dubrowski

Bibliographic record

VenueCureus · 2020
Typearticle
Languageen
FieldMedicine
TopicSimulation-Based Education in Healthcare
Canadian institutionsMemorial University of NewfoundlandUniversity of Ontario Institute of Technology
Fundersnot available
KeywordsMedicineContext (archaeology)Coronavirus disease 2019 (COVID-19)Mechanical ventilatorEconomic shortageVentilation (architecture)Health professionals3d printedHealth careMedical emergencyMechanical ventilationMechanical engineeringEngineering

Abstract

fetched live from OpenAlex

Recent surges in COVID-19 cases have generated an urgent global demand for ventilators. This demand has led to the development of numerous low-cost ventilation devices, but there has been less emphasis on training health professionals to use these new devices safely. The aim of this technical report is twofold: first, to describe the design and manufacturing process of the automated inflating resuscitator (AIR), a 3D-printed ventilator training device which operates on the principle of pushing a bag valve mask; second, to present a simulation scenario that can be used for training health professionals how to use this and similar, low-cost, 3D-printed ventilators in the context of ventilator shortages caused by COVID-19. To this end, the AIR was designed in an expedient manner in accordance with basic functionality established by the Medicines and Healthcare Products Regulatory Agency (United Kingdom) for provisional clinical use in light of COVID-19.

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: Simulation or modeling · Consensus signal: Simulation or modeling
GenreCandidate signal: Empirical · Consensus signal: Empirical
Teacher disagreement score0.432
Threshold uncertainty score0.374

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 head0.104
GPT teacher head0.390
Teacher spread0.286 · 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

Citations12
Published2020
Admission routes1
Has abstractyes

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