Bibliographic record
Abstract
Introduction:The EAU currently recommends partial nephrectomy for localized cT1 renal tumors.With the advent of robotic-assisted partial nephrectomy (RAPN), there is growing evidence that warm ischemia time may be reduced compared to the traditional laparoscopic-assisted partial nephrectomy (LAPN).The current study aimed to reduce inter-operator bias while maintaining an adequate sample size to assess the differences in outcomes between the two approaches using a single-operator experienced in both approaches.Methods: We retrospectively chart reviewed all partial nephrectomies undertaken by a single surgeon from 2019-2021.Patient demographics (such as age, weight, GFR), as well as perioperative outcomes (such as operation time, blood loss, hemoglobin drop, warm ischemic time) and postoperative outcomes (such as length of stay) were collected.Results: A total of 95 cases were retrieved for inclusion in this study (46 RAPN, 49 LAPN).There were no significant differences in patient demographics or comorbidities.RAPN was associated with significantly reduced mean operative time (142 vs. 157 minutes), warm ischemic clamp time (13.9 vs. 16.5 minutes), and mean hospital stay (2.4 vs. 3.7 days).RAPN was also associated with a reduced drop in postoperative day 1 GFR (6.1 vs. 13.5);however, a significant long-term improvement in GFR was not seen.Conclusions: RAPN is a safe and effective alternate to LAPN.RAPN may reduce operative times and warm ischemic times leading to improved renal function preservation in the short term.Further prospective studies and cost-benefit analysis of robotic-assisted partial nephrectomy would be valuable in confirming these findings and justifying the use against their financial cost.
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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 | 0.001 |
| Meta-epidemiology (narrow) | 0.001 | 0.000 |
| Meta-epidemiology (broad) | 0.001 | 0.001 |
| Bibliometrics | 0.001 | 0.001 |
| Science and technology studies | 0.001 | 0.000 |
| Scholarly communication | 0.001 | 0.001 |
| Open science | 0.001 | 0.002 |
| Research integrity | 0.001 | 0.001 |
| Insufficient payload (model declined to judge) | 0.215 | 0.065 |
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".