3D printing in medicine of congenital heart diseases
Bibliographic record
Abstract
Congenital heart diseases causing significant hemodynamic and functional consequences require surgical repair. Understanding of the precise surgical anatomy is often challenging and can be inadequate or wrong. Modern high resolution imaging techniques and 3D printing technology allow 3D printing of the replicas of the patient's heart for precise understanding of the complex anatomy, hands-on simulation of surgical and interventional procedures, and morphology teaching of the medical professionals and patients. CT or MR images obtained with ECG-gating and breath-holding or respiration navigation are best suited for 3D printing. 3D echocardiograms are not ideal but can be used for printing limited areas of interest such as cardiac valves and ventricular septum. Although the print materials still require optimization for representation of cardiovascular tissues and valves, the surgeons find the models suitable for practicing closure of the septal defects, application of the baffles within the ventricles, reconstructing the aortic arch, and arterial switch procedure. Hands-on surgical training (HOST) on models may soon become a mandatory component of congenital heart disease surgery program. 3D printing will expand its utilization with further improvement of the use of echocardiographic data and image fusion algorithm across multiple imaging modalities and development of new printing materials. Bioprinting of implants such as stents, patches and artificial valves and tissue engineering of a part of or whole heart using the patient's own cells will open the door to a new era of personalized medicine.
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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 | 0.001 |
| Meta-epidemiology (narrow) | 0.001 | 0.000 |
| Meta-epidemiology (broad) | 0.001 | 0.001 |
| Bibliometrics | 0.004 | 0.003 |
| Science and technology studies | 0.000 | 0.001 |
| Scholarly communication | 0.002 | 0.002 |
| Open science | 0.001 | 0.001 |
| Research integrity | 0.002 | 0.002 |
| Insufficient payload (model declined to judge) | 0.007 | 0.004 |
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".