Expanding the use of 3D printing in congenital heart surgery
Bibliographic record
Abstract
Three-dimensional (3D) printing has evolved the way patients with complex congenital heart disease are managed. The main applications of 3D printing in congenital heart surgery are (I) clinical decision making/surgical planning; (II) practice/simulation; (III) education. This paper explores these applications and how they can benefit clinicians involved in the care of patients with complex congenital heart disease. 3D printing is particularly useful in clinical decision making for patients with complex diseases, especially when there is ambiguity over whether a biventricular repair is possible versus univentricular palliation. This paper provides a summary of the evidence that supports its use and in which subset of patients this technology is most beneficial. Beyond surgical planning, 3D models support the safe development and rehearsal of novel surgical techniques and provide a platform for simulation-based training. Studies have shown that simulation using 3D models improves technical performance and procedural efficiency, supporting their integration into training curricula, particularly in resource-limited settings. Furthermore, 3D models enhance education for medical professionals and aid communication with patients and families, facilitating shared decision-making. Although high-quality evidence supporting 3D printing is limited, in the clinical management of patients with complex congenital heart disease, 3D printing should be considered in complex cases. This clinical benefit is being supported by the availability of reimbursement codes in some countries. As the technology advances, 3D printing may become a core component in congenital heart surgery. While further evidence is needed to fully quantify its clinical benefit, current data and global experience strongly support its integration into care pathways for selected patients, training programs, and education strategies.
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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 | 0.007 | 0.019 |
| Meta-epidemiology (narrow) | 0.001 | 0.000 |
| Meta-epidemiology (broad) | 0.001 | 0.002 |
| Bibliometrics | 0.003 | 0.002 |
| Science and technology studies | 0.000 | 0.002 |
| Scholarly communication | 0.003 | 0.003 |
| Open science | 0.001 | 0.003 |
| Research integrity | 0.002 | 0.002 |
| Insufficient payload (model declined to judge) | 0.010 | 0.002 |
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".