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Record W4386910683 · doi:10.1111/medu.15210

Paediatric residents painting 3D congenital heart disease models

2023· article· en· W4386910683 on OpenAlexaff
Jared A. Sheridan, G. Slim, Jessica L. Foulds, Carolina A. Escudero

Bibliographic record

VenueMedical Education · 2023
Typearticle
Languageen
FieldEngineering
TopicAnatomy and Medical Technology
Canadian institutionsStollery Children's Hospital
Fundersnot available
KeywordsHeart diseaseMedicinePaintingPediatricsCardiologyArtVisual arts

Abstract

fetched live from OpenAlex

Congenital heart defects (CHDs) are the most common congenital anomaly affecting 1% of newborns, with 8% of CHDs having only one effective or ‘single’ ventricle. It is critical that paediatricians and paediatric residents understand cardiac defects, both anatomically and physiologically, as these lesions are commonly encountered in clinical practice. CHDs can be complex and challenging to understand. Many current educational tools for learning about CHDs have limited interactive qualities and may not support kinesthetic learning preferences. 3D printing technology is increasingly affordable, accessible, and can create highly accurate models of CHDs from CT or MRI scans.1 We printed 3D models of the three stages of single ventricle palliation (Stage 1: Norwood procedure for hypoplastic left heart syndrome, Stage 2: Glenn procedure, Stage 3: Fontan procedure), which are anatomically complex CHDs. These defects were chosen as each stage demonstrates significant changes in physiology which correspond to clinical manifestations in patients. We printed the models using a white firm plastic material (polylactic acid or PLA) suitable for acrylic paint application. We provided a 1 hour teaching session starting with a 20-minute didactic portion orienting the learners to the 3D models and demonstrating the cardiac anatomy via presentation by a paediatric cardiologist, followed by a 40-minute interactive portion where groups of three residents each painted a 3D model corresponding to one of the stages of the single ventricle palliation with red, blue and purple paint. Residents were instructed to use paint to demonstrate relative oxygen saturations of the blood in the different areas of the heart: red for oxygenated blood, blue for deoxygenated blood, and purple for mixed or partially oxygenated blood. Digital versions of the 3D model with the corresponding red, blue, and purple colouring (Stage 1: https://skfb.ly/otyZA; Stage 2: https://skfb.ly/otAUX; Stage 3: https://skfb.ly/oAOyV) and physical models with this colouring (Vero material using Stratasys J750 printer) were provided as guides. Two paediatric cardiology fellows circulated to answer questions and guide individual residents. Residents were encouraged to discuss their models within their groups to compare the different stages of the single ventricle palliation and could keep their painted model. Thirty-eight residents participated. We found that this novel method of interactive teaching was feasible and appeared enjoyable and informative for paediatric trainees. Many residents remained beyond the allotted time to continue their conversations or to finish painting their model, suggesting that this was an interesting educational session for the residents and that an increased time allotment for the painting activity would be valuable. We observed resident engagement via discussions within their groups, comparison of the models of different stages, and residents asking questions about the models and the clinical implications of each stage of the single ventricle palliation. The session required significant time investment to print models and prepare painting materials, but overall material costs were not prohibitive as our institution owned the 3D printer. Given the resident engagement and positive feedback, we intend to repeat similar sessions focusing on different CHDs.

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: none
GenreCandidate signal: Empirical · Consensus signal: Empirical
Teacher disagreement score0.751
Threshold uncertainty score0.462

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 head0.008
GPT teacher head0.260
Teacher spread0.252 · 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".

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Citations1
Published2023
Admission routes1
Has abstractyes

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