The Use of Three-Dimensional Printing in Cardiac Structural Disease: A Review
Bibliographic record
Abstract
OBJECTIVE: Three-dimensional printing (3DP), or additive fabrication, is a process in which a physical 3D model is created using a multitude of 2-dimensional images. This process has been applied to numerous surgical subspecialties with growing interest for the use of 3DP in adult structural heart disease. This scoping review evaluates the use of 3DP in transcatheter and surgical aortic and mitral valve interventions as well as left atrial appendage occlusion in terms of its practical and clinical application. METHODS: Articles were identified through PubMed and Embase using MeSH search terms as well as independent searches. A total of 645 articles were screened, and 37 were retained for qualitative analysis. RESULTS: Operative planning was coded in 100% of articles, complication prevention in 43%, medical education in 5.4%, patient education in 0%, and simulation in 5.4%. CONCLUSIONS: The main uses of 3DP in acquired structural heart disease are centered around operative planning and complication prevention, with moderate use regarding surgical simulation and infrequent use regarding medical and/or patient education. Although patient anatomy varies greatly, deploying 3DP as a large-scale tool remains a possibility. The more 3D models are made, the more can be learned about demographic subsets of patient populations. Due to the lack of standard operating procedures for the creation of 3DP models, the cost-effectiveness of these models is hard to determine and likely center specific. More research into this facet could inform centers that wish to implement this tool.
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 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 | 0.003 | 0.010 |
| Meta-epidemiology (narrow) | 0.001 | 0.001 |
| Meta-epidemiology (broad) | 0.002 | 0.002 |
| Bibliometrics | 0.009 | 0.009 |
| Science and technology studies | 0.001 | 0.001 |
| Scholarly communication | 0.002 | 0.002 |
| Open science | 0.001 | 0.001 |
| Research integrity | 0.002 | 0.001 |
| 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".