Utilizing 3D printing to facilitate surgical in-situ paediatric renal artery aneurysm repair for refractory hypertension
Bibliographic record
Abstract
Renal artery aneurysmal (RAA) disease is a rare, but potentially life-threatening cause of renovascular disease presenting with hypertension. Conventional management involves aneurysmal excision followed by renal auto-transplantation. We present the management of a 13-year-old girl with complex multiple saccular aneurysmal disease of the left renal artery with hilar extension and symptomatic hypertension. We used 3D printing to print a patient-specific model that was not implanted in the patient but was used for surgical planning and discussion with the patient and their family. Endovascular options were precluded due to anatomical complexities. Following multi-disciplinary review and patient-specific 3D printing, she underwent successful in-situ RAA repair with intraoperative cooling, without the need for auto-transplantation. 3D printing enabled appreciation of aneurysmal spatial configuration and dimensions that also helped plan the interposition graft length needed following aneurysmal excision. The models provided informed multidisciplinary communications and proved valuable during the consent process with the family for this high-risk procedure. To our knowledge, this is the first reported case utilizing 3D printing to facilitate in-situ complex repair of RAA with intra-hilar extension for paediatric renovascular disease.
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.
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 | 0.002 |
| Meta-epidemiology (narrow) | 0.001 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.001 |
| Bibliometrics | 0.001 | 0.000 |
| Science and technology studies | 0.000 | 0.001 |
| Scholarly communication | 0.001 | 0.001 |
| Open science | 0.001 | 0.001 |
| Research integrity | 0.001 | 0.001 |
| Insufficient payload (model declined to judge) | 0.003 | 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".