Cardiothoracic Applications of 3-dimensional Printing
Why this work is in the frame
A frame that forgets how it found something cannot be audited. These are the routes that admitted this work.
Bibliographic record
Abstract
Medical 3-dimensional (3D) printing is emerging as a clinically relevant imaging tool in directing preoperative and intraoperative planning in many surgical specialties and will therefore likely lead to interdisciplinary collaboration between engineers, radiologists, and surgeons. Data from standard imaging modalities such as computed tomography, magnetic resonance imaging, echocardiography, and rotational angiography can be used to fabricate life-sized models of human anatomy and pathology, as well as patient-specific implants and surgical guides. Cardiovascular 3D-printed models can improve diagnosis and allow for advanced preoperative planning. The majority of applications reported involve congenital heart diseases and valvular and great vessels pathologies. Printed models are suitable for planning both surgical and minimally invasive procedures. Added value has been reported toward improving outcomes, minimizing perioperative risk, and developing new procedures such as transcatheter mitral valve replacements. Similarly, thoracic surgeons are using 3D printing to assess invasion of vital structures by tumors and to assist in diagnosis and treatment of upper and lower airway diseases. Anatomic models enable surgeons to assimilate information more quickly than image review, choose the optimal surgical approach, and achieve surgery in a shorter time. Patient-specific 3D-printed implants are beginning to appear and may have significant impact on cosmetic and life-saving procedures in the future. In summary, cardiothoracic 3D printing is rapidly evolving and may be a potential game-changer for surgeons. The imager who is equipped with the tools to apply this new imaging science to cardiothoracic care is thus ideally positioned to innovate in this new emerging imaging modality.
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.
Full frame distilled prediction
Teacher imitationNot calibrated prevalence, not ground truth. Human validation pending. Learned from the 10,348 direct Codex labels and 10,348 direct Gemma labels. Candidate is the union of thresholded teacher heads; consensus is their intersection. These outputs are machine_predicted_unvalidated and are not human labels or direct frontier model labels.
Codex and Gemma teacher scores by category
| Category | Codex | Gemma |
|---|---|---|
| Metaresearch | 0.001 | 0.000 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.001 | 0.001 |
| Bibliometrics | 0.001 | 0.000 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.000 | 0.000 |
| Open science | 0.000 | 0.000 |
| Research integrity | 0.000 | 0.001 |
| Insufficient payload (model declined to judge) | 0.000 | 0.000 |
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 it