Painting of 3D Models of Congenital Heart Disease as an Educational Tool for Pediatric Residents
Bibliographic record
Abstract
Background Three-dimensional (3D) models of congenital heart diseases have been used to teach cardiac anatomy to learners. Our team sought to determine whether the painting of 3D models could help pediatric residents better understand both the anatomy and physiology of complex congenital heart diseases. Methods This was a prospective assessment of pediatric resident perceptions regarding a novel teaching method on the 3 stages of palliation for hypoplastic left heart syndrome. After attending a didactic session about the topic, they were provided with 3D models representing each stage. They were guided in painting them to represent the presumed oxygen saturations of each model's chambers. Questionnaires were used to assess the self-perceived understanding of the anatomy, pathophysiology, and management from before to after the session using 5-point Likert scales. Statistical analysis was performed using a paired samples t test. Results There were 36 pediatric residents from 2 institutions. There was an increase in the mean self-perceived understanding of the anatomy (2.14 vs 3.94, P < 0.001), pathophysiology (2.14 vs 3.72, P < 0.001), and management of single ventricle palliation (2.19 vs 3.78, P < 0.001) before and after the session. All 36 participants enjoyed the session and wanted to participate in future sessions. Conclusion Painting of 3D models increased knowledge acquisition among pediatric residents.
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How this classification was reachedexpand
Full frame machine prediction
Teacher imitationNot 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.
Distilled classifier scores by category (both heads)
| Category | Codex | Gemma |
|---|---|---|
| Metaresearch | 0.001 | 0.004 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.000 | 0.000 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.001 | 0.000 |
| Open science | 0.000 | 0.001 |
| Research integrity | 0.000 | 0.001 |
| Insufficient payload (model declined to judge) | 0.005 | 0.000 |
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.
score_only:v0-immature-baseline · verbatim from the scoring run: score_only means the number may rank works, and no category label ships from itClassification
machine, unvalidatedMachine predicted; a candidate call from one source (direct Gemma or distilled Codex), not a consensus.
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".