Robotic-assisted laparoscopic partial nephrectomy vs. laparoscopic and open partial nephrectomy
Bibliographic record
Abstract
INTRODUCTION: In 2019, our center attempted to transition all partial nephrectomies (PNs) to robotic-assisted laparoscopic PN (RALPN). The purpose of this study was to compare RALPN outcomes to laparoscopic PN (LPN) and open PN (OPN) at our institution, as there is limited literature from Canadian centers. METHODS: In this single-center, two-surgeon, retrospective cohort study, we compared RALPN outcomes during the early phase of our robotics program to OPN and LPN performed just before the introduction of RALPN. RESULTS: A total of 106 patients underwent OPN, 83 LPN, and 82 RALPN during the study period. Median RALPN REN AL score was 7 vs. 6 for LPN (p<0.05) and 8 for OPN (p=0.10). Median RALPN length of stay (LOS) was two days vs. three and four days for LPN and OPN (p<0.05), respectively. OPN median procedure time was 104 minutes vs. 94 and 82 minutes for LPN and RALPN (p<0.05), respectively. Median OPN operating room (OR) time was 160 minutes vs. 150 and 146 minutes for LPN and RALPN (p<0.05), respectively. There were no significant differences in intraoperative (p=0.92) or postoperative complication rates (p=0.47). RALPN warm ischemia time (WIT) was 17 minutes vs. 14.5 and 15 minutes for OPN and LPN (p<0.05), respectively. Median RALPN estimated blood loss (EBL) was 165 ml vs. 250 ml for OPN (p<0.05) and 125 ml for LPN (p=0.15). CONCLUSIONS: Although patients who underwent RALPN had longer WIT, they had similar rates of complications, required less total OR time, and had shorter procedure times and LOS compared with OPN and LPN despite similar REN AL scores compared to OPN and greater scores than LPN.
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) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.001 |
| Bibliometrics | 0.000 | 0.000 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.000 | 0.000 |
| Open science | 0.000 | 0.000 |
| Research integrity | 0.000 | 0.000 |
| Insufficient payload (model declined to judge) | 0.002 | 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".