Utility of 3D printed cardiac models in congenital heart disease: a scoping review
Bibliographic record
Abstract
OBJECTIVE: Three-dimensional printing (3DP) is a novel technology with applications in healthcare, particularly for congenital heart disease (CHD). We sought to explore the spectrum of use of 3D printed CHD models (3D-CM) and identify knowledge gaps within the published body of literature to guide future research. METHODS: We conducted a scoping review targeting published literature on the use of 3D-CMs. The databases of MEDLINE, EMBASE and Web of Science were searched from their inception until 19 July 2019. Inclusion criteria were primary research; studies reporting use of 3D-CMs; and human subjects. Exclusion criteria were studies where 3D-CMs were generated for proof of concept but not used; and studies focused on bioprinting or computational 3D-CMs. Studies were assessed for inclusion and data were extracted from eligible articles in duplicate. RESULTS: The search returned 648 results. Following assessment, 79 articles were included in the final qualitative synthesis. The majority (66%) of studies are case reports or series. 15% reported use of a control group. Three main areas of utilisation are for (1) surgical and interventional cardiology procedural planning (n=62), (2) simulation (n=25), and (3) education for medical personnel or patients and their families (n=17). Multiple studies used 3D-CMs for more than one of these areas. CONCLUSIONS: 3DP for CHD is a new technology with an evolving literature base. Most of the published literature are experiential reports as opposed to manuscripts on scientifically robust studies. Our study has identified gaps in the literature and addressed priority areas for future research.
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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 | 0.016 | 0.072 |
| Meta-epidemiology (narrow) | 0.002 | 0.001 |
| Meta-epidemiology (broad) | 0.005 | 0.006 |
| Bibliometrics | 0.022 | 0.017 |
| Science and technology studies | 0.001 | 0.002 |
| Scholarly communication | 0.006 | 0.004 |
| Open science | 0.003 | 0.003 |
| Research integrity | 0.004 | 0.002 |
| Insufficient payload (model declined to judge) | 0.005 | 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".