3D Modeling and Printing in Congenital Heart Surgery: Entering the Stage of Maturation
Bibliographic record
Abstract
3D printing allows the most realistic perception of the surgical anatomy of congenital heart diseases without the requirement of physical devices such as a computer screen or virtual headset. It is useful for surgical decision making and simulation, hands-on surgical training (HOST) and cardiovascular morphology teaching. 3D-printed models allow easy understanding of surgical morphology and preoperative surgical simulation. The most common indications for its clinical use include complex forms of double outlet right ventricle and transposition of the great arteries, anomalous systemic and pulmonary venous connections, and heterotaxy. Its utility in congenital heart surgery is indisputable, although it is hard to "scientifically" prove the impact of its use in surgery because of many confounding factors that contribute to the surgical outcome. 3D-printed models are valuable resources for morphology teaching. Educational models can be produced for almost all different variations of congenital heart diseases, and replicated in any number. HOST using 3D-printed models enables efficient education of surgeons in-training. Implementation of the HOST courses in congenital heart surgical training programs is not an option but an absolute necessity. In conclusion, 3D printing is entering the stage of maturation in its use for congenital heart surgery. It is now time for imagers and surgeons to find how to effectively utilize 3D printing and how to improve the quality of the products for improved patient outcomes and impact of education and training.
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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 | 0.002 | 0.002 |
| Meta-epidemiology (narrow) | 0.001 | 0.000 |
| Meta-epidemiology (broad) | 0.001 | 0.001 |
| Bibliometrics | 0.003 | 0.003 |
| Science and technology studies | 0.000 | 0.002 |
| Scholarly communication | 0.002 | 0.003 |
| Open science | 0.001 | 0.001 |
| Research integrity | 0.002 | 0.003 |
| Insufficient payload (model declined to judge) | 0.003 | 0.001 |
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.
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".