New Generation of 3D Virtual Models with Perfusional Zones: Perioperative Assistance for the Best Pedicle Management during Robotic Partial Nephrectomy
Bibliographic record
Abstract
Selective clamping during robot-assisted partial nephrectomy (RAPN) may reduce ischemia-related functional impairment. The intraoperative use of 3D-virtual models (3DVMs) can improve surgical planning, resulting in a greater success rate for selective clamping. Our goal is to introduce a new generation of 3DVMs, which consider the perfusion volumes of the kidney. Patients listed for RAPN from 2021 to 2022 were recruited. A selective clamping strategy was designed and intraoperatively performed based on the specifically generated 3DVMs. The effectiveness of selective clamping was evaluated using near-infrared-fluorescence imaging (NIRF) and 3DVM. Perfusion areas extensions were compared, and relevant preoperative characteristics were analyzed. In 61 of 80 (76.25%) cases, selective clamping was performed. The concordance between the 3DVM areas and the NIRF-enhanced areas was verified (k = 0.91). According to the distribution of perfused areas crossing the tumor, there were one, two, three, four, and five crossing areas, with relative perfusion rates of 13.75%, 35%, 32.5%, 13.75%, and 5%, respectively. Lesion diameter and mesorenal location were the only factors related to a higher number (>3) of perfusion volumes crossing the lesion. The implementation of mathematical algorithms to 3DVMs allows for precise estimation of the perfusion zone of each arterial branch feeding the organ, leading to the performance of safe and effective pedicle management planning.
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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 | 0.002 |
| Meta-epidemiology (narrow) | 0.001 | 0.001 |
| Meta-epidemiology (broad) | 0.000 | 0.001 |
| Bibliometrics | 0.001 | 0.000 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.001 | 0.001 |
| Open science | 0.001 | 0.001 |
| Research integrity | 0.000 | 0.000 |
| Insufficient payload (model declined to judge) | 0.001 | 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 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".