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Record W3024582444 · doi:10.1017/cem.2020.176

MP28: Development and validation of a novel three-dimensional printed thorax model simulator for the simulation-based training of tube thoracostomy

2020· article· en· W3024582444 on OpenAlexaff
V. Brannan, Cody Dunne, Adam Dubrowski, Michael H. Parsons

Bibliographic record

VenueCanadian Journal of Emergency Medicine · 2020
Typearticle
Languageen
FieldMedicine
TopicSurgical Simulation and Training
Canadian institutionsMemorial University of Newfoundland
Fundersnot available
KeywordsThoracostomyFace validitySimulationMedicineContent validityComputer scienceMedical physicsSurgery

Abstract

fetched live from OpenAlex

Innovation Concept: High-acuity, low-occurrence (HALO) procedures require skilled performance as they treat life-threatening conditions and are associated with significant morbidity when performed incorrectly. Simulation has proven useful for deliberate practice in a low stake setting. Tube thoracostomy is amendable to this approach. Commercially available trainers exist but often have limited realism and are prohibitively expensive particularly to non-academic centers. Three-dimensional (3D) printing produces models suitable for simulation, but no current simulator has been developed and validated for tube thoracostomy. The aim of this study was to develop such, a 3D-printed low-fidelity simulator validated for the simulation-based instruction of tube thoracostomy. Methods: The development of the simulator followed an iterative design cycle with collaboration between a design team and an emergency medicine expert. Its validity (face and content) was tested through hands-on practice and surveys completed by 15 acute-care practitioners. Participants performed the procedure on the simulator and then provided feedback through a mixed quantitative/qualitative product evaluation survey on appearance, realism (face validity) and value in procedural training (content validity). Mean values for overall appearance and content validity as a training tool were 4/5 and 4.3/5 respectively. All respondents felt the model was a useful adjunct. All but one stated it was a good replacement for pre-existing trainers. Curriculum, Tool, or Material: The model was initially printed in three parts using an Ultimaker 3 and Axiom Airwolf Dual 3D-printer. The ribcage was created using polylactic acid with polyvinyl alcohol support material. Printed sections were bonded using glue at interfaces requiring no flexibility. Flexible joints were made of varying amounts of thermoplastic polyurethane and thermoplastic elastomer. Skin overlay for the whole model was created with a cut out area for replaceable sections that subjects would incise to insert the chest tube. Skin was casted using platinum cured silicone in a 3D-printed mold. Total cost of all materials was roughly 80 CAD. Conclusion: The simulator was found to be a useful adjunct for the simulation-based practice of tube thoracostomy. As well, users found the model anatomically realistic and avoided high-cost and ethical issues. Further research will focus on optimization based on feedback and development into a multi-functional simulator for other HALO procedures.

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)0.004
metaresearch head score (Gemma)0.007
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 score0.004
Threshold uncertainty score0.020

Distilled classifier scores by category (both heads)

CategoryCodexGemma
Metaresearch0.0040.007
Meta-epidemiology (narrow)0.0010.000
Meta-epidemiology (broad)0.0000.001
Bibliometrics0.0010.000
Science and technology studies0.0000.001
Scholarly communication0.0010.001
Open science0.0020.001
Research integrity0.0010.001
Insufficient payload (model declined to judge)0.0040.001

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 head0.258
GPT teacher head0.380
Teacher spread0.122 · 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

Citations0
Published2020
Admission routes1
Has abstractyes

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