3D printing: a useful tool for safe clinical practice in children with complex vasculature
Bibliographic record
Abstract
BACKGROUND: 3D printing has been used in different medical contexts, although it is underutilised in paediatrics. We present the first use of 3D printing in the management of three paediatric patients with complex renovascular disease. METHODS: Patient-specific 3D models were produced from conventional 2D imaging and manufactured using 3D polyjet printing technology. All three patients had different underlying pathologies, but all underwent multiple endovascular interventions (renal artery balloon angioplasty) prior to 3D printing and subsequent vascular surgery. The models were verified by an expert radiologist and then presented to the multidisciplinary team to aid with surgical planning. RESULTS: Following evaluation of the 3D-printed models, all patients underwent successful uni/bilateral renal auto-transplants and aortic bypass surgery. The 3D models allowed more detailed preoperative discussions and more focused planning of surgical approach, therefore enhancing safer surgical planning. It influenced clinical decision-making and shortened general anaesthetic time. The families and the patients reported that they had a significantly improved understanding of the patient's condition and had more confidence in understanding proposed surgical intervention, thereby contributing to obtaining good-quality informed consent. CONCLUSION: 3D printing has a great potential to improve both surgical safety and decision-making as well as patient understanding in the field of paediatrics and may be considered in wider surgical areas.
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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 | 0.011 |
| Meta-epidemiology (narrow) | 0.001 | 0.000 |
| Meta-epidemiology (broad) | 0.001 | 0.001 |
| Bibliometrics | 0.002 | 0.001 |
| Science and technology studies | 0.001 | 0.002 |
| Scholarly communication | 0.003 | 0.002 |
| Open science | 0.001 | 0.003 |
| Research integrity | 0.002 | 0.002 |
| Insufficient payload (model declined to judge) | 0.011 | 0.003 |
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".