The Influence of 3D Technology Integration on Laparoscopic Partial Nephrectomy Practice and Surgical Outcomes
Bibliographic record
Abstract
Partial nephrectomy (PN) is the standard treatment for renal cell carcinoma (RCC), offering cancer control with renal preservation. Three-dimensional laparoscopy addresses the limitations of traditional two-dimensional systems by enhancing depth perception and accuracy. This study evaluates the impact of 3D laparoscopy on PN for larger and complex tumors. We retrospectively analyzed 200 laparoscopic nephrectomies by a single surgeon between 2020 and 2024, comparing pre-3D and post-3D groups (100 cases each). Key outcomes included the rate of PN, warm ischemia time (WIT), and operative time. The post-3D group demonstrated a significant increase in PN for tumors >4 cm (48% vs. 35%, p = 0.028) and high RENAL scores ≥8 (41% vs. 29%, p = 0.035). Median WIT was significantly shorter (24 min vs. 29 min, p = 0.018 for larger tumors; 26 min vs. 32 min, p = 0.022 for high complexity). Total operative time was also reduced (175 min vs. 195 min, p = 0.031). Positive surgical margins were lower in the post-3D group (0% vs. 2%), and complication rates were comparable (5% vs. 4%, p = 0.712). Three-dimensional laparoscopy significantly improves the feasibility and precision of PN for larger and complex tumors, enhancing outcomes without increasing complications.
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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 | 0.007 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.001 |
| Bibliometrics | 0.001 | 0.001 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.001 | 0.001 |
| Open science | 0.000 | 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".