Comparative Analysis of Robotic-Assisted Partial Nephrectomy Versus Open Partial Nephrectomy During the Initial Robotic Learning Curve: Does the End Justify the Means?
Bibliographic record
Abstract
Background: Several studies have alluded to a detrimental impact of the surgeon’s “learning curve” on outcomes of minimally invasive surgery. In this study, we evaluated the outcomes of robotic-assisted partial nephrectomy (RAPN) versus open partial nephrectomy (OPN) for kidney tumors, during the introduction of Robotic Urologic Oncology at our institution. Methods: A retrospective review of all consecutive partial nephrectomies (PN), RAPN and OPN, performed at the American University of Beirut Medical Center since the inception of the robotic program in July 2013 until July 2015. Thirty-four consecutive patients underwent PNs, 19 OPN and 15 RAPN. Preoperative variables (patient characteristics, tumor size, and RENAL score) and perioperative renal functional/patient outcomes (% change in glomerular filtration rate (GFR), ischemia time, blood loss, need for blood transfusions, total operating time, and length of hospital stay) were compared using SPSS. Results: Preoperative variables, including the size and RENAL score of the tumor were analyzed. The difference in the median size of the tumor between OPN and RAPN was not statistically significant (4.5 ± 2.7 cm vs. 3.6 ± 1.7 cm, respectively, P = 0.25). RENAL score was significantly higher for OPN compared to RAPN (7.3 ± 2.3 vs. 4.9 ± 1.5, respectively, P < 0.05). Mean operative time was significantly shorter for OPN vs. RAPN (178 ± 52 min vs. 296 ± 86 min, respectively, P < 0.05). Cold ischemia time was 24 ± 3 min in OPN, and warm ischemia time was 17.5 ± 2 min for RAPN; 10 out of the total 15 robotic cases were performed with a warm ischemia time of < 20 min. Intraoperative blood loss was comparable for both approaches (225 ± 132 mL in OPN vs. 243 ± 192 mL in RAPN), and there was no need for blood transfusions in either group. Hospital stay was significantly longer for OPN vs. RAPN (6 ± 1.6 days vs. 4 ± 0.9 days, respectively, P = 0.01). The change in GFR was comparable among both procedures (OPN = -9% vs. RAPN = -7%); pathological margin status was also comparable among both procedures, with 1/19 (5%) positive focal margins in OPN vs. 0/14 in RAPN. None of the robotic procedures required conversion to the laparoscopic or open approach. Conclusions: RAPN is currently an established approach for the treatment of kidney tumors with the advantages of decreased crude ischemia time and a shorter hospital stay, with comparable intraoperative blood loss and risk of GFR reduction. Our data show that tumor characteristics were not equivalent, with higher RENAL scores noted in patients allocated to OPN vs. RAPN, thus limiting a fair comparison of outcomes. However, the data confirm that with proper selection of patients for RAPN, outcomes were equivalent to OPN and were not jeopardized during the initial robotic learning curve. Larger prospective studies are needed to validate our results. World J Nephrol Urol. 2016;5(4):79-82 doi: https://doi.org/10.14740/wjnu286w
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How this classification was reachedexpand
Full frame distilled prediction
Teacher imitationNot calibrated prevalence, not ground truth. Human validation pending. Learned from the 10,348 direct Codex labels and 10,348 direct Gemma labels. Candidate is the union of thresholded teacher heads; consensus is their intersection. These outputs are machine_predicted_unvalidated and are not human labels or direct frontier model labels.
Codex and Gemma teacher scores by category
| Category | Codex | Gemma |
|---|---|---|
| Metaresearch | 0.001 | 0.000 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.001 | 0.000 |
| Bibliometrics | 0.000 | 0.001 |
| Science and technology studies | 0.001 | 0.002 |
| Scholarly communication | 0.000 | 0.000 |
| Open science | 0.001 | 0.000 |
| Research integrity | 0.000 | 0.001 |
| 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 teacher head, 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".