Trends of 3D bioprinting in vascular surgery
Bibliographic record
Abstract
Nowadays, surgical planning is recognized as one of the most useful applications of three-dimensional (3D) printing.It has been demonstrated that 3D models may assist to overcome the surgical challenges of complex vascular anatomy and improve the endovascular skills required in certain procedures.Therefore, reproducing a patient based anatomical 3D model act as a tool for individualized preoperative planning and decision making with a direct positive impact in the clinical outcomes.Another interesting field concerning vascular surgery and bioprinting, is the possibility of developing a variety of prosthetic devices for treating vascular disease.The main objective is to overcome biocompatibility disadvantages of prosthesis made from synthetic fabrics among other shortcomings.These may include, long manufacturing times and the high costs of an individualized prosthetic device, challenges faced when an autologous vein is not available.Unfortunately, cases requiring this sophisticated management are usually faced in the context of emergency care with a limited number of therapeutic options and a high mortality rate.Understanding the complexity of vessels biology; such as the interactions between each layer of the vessel wall, is extremely important for making a 3D-printed vessel which could, in the close future, simulate a real human vessel.Achieving this would mean more availability and in consequence, cost reduction for treating complex vascular disease.These benefits would be reflected not only in lowering medical and hospital expenses, but also in the morbidity and mortality related to the surgical procedure.
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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 | 0.003 | 0.003 |
| Meta-epidemiology (narrow) | 0.001 | 0.000 |
| Meta-epidemiology (broad) | 0.001 | 0.001 |
| Bibliometrics | 0.003 | 0.003 |
| Science and technology studies | 0.001 | 0.002 |
| Scholarly communication | 0.003 | 0.002 |
| Open science | 0.001 | 0.001 |
| Research integrity | 0.002 | 0.002 |
| Insufficient payload (model declined to judge) | 0.007 | 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".