Perioperative and oncological outcomes of robot-assisted laparoscopic partial nephrectomy for cystic and solid renal masses: Evidence from controlled trials
Bibliographic record
Abstract
To evaluate the outcomes of robot-assisted partial nephrectomy (RAPN) for solid and cystic renal tumors. We systematically searched the Cochrane Library, PubMed, EMBASE, and Scopus databases up to March 2023. Review Manager 5.4 performed a pooled analysis of the data for random effects. Besides, sensitivity and subgroup analyses to explore heterogeneity, Newcastle-Ottawa scale, and GRADE to evaluate study quality and level of evidence. Five observational studies comprising 1353 patients (Cystic tumor: 183; Solid tumor: 1083) were included in this study. Compared to solid masses, cystic masses were associated with fewer major complications (odds ratio [OR] = 2.2; 95% confidence intervals [CI] = 1.17 to 4.13; p = 0.01). Additionally, no significant differences were observed between the two groups in terms of operative time, warm ischemia time, blood loss, hospital stay, intraoperative complications, postoperative complications, transfusion rate, postoperative estimated glomerular filtration rate (eGFR), eGFR preservation, positive surgical margin (PSM), recurrence, overall survival (OS), cancer-specific survival (CSS), recurrence-free survival (RFS) and trifecta achievement. RAPN can be performed in cystic renal tumors with perioperative, functional, and oncologic outcomes like those achievable in solid tumors. However, our findings need further validation in a large-sample prospective randomized study.
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 | 0.012 | 0.045 |
| Meta-epidemiology (narrow) | 0.001 | 0.001 |
| Meta-epidemiology (broad) | 0.010 | 0.013 |
| Bibliometrics | 0.003 | 0.004 |
| Science and technology studies | 0.000 | 0.001 |
| Scholarly communication | 0.003 | 0.002 |
| Open science | 0.002 | 0.001 |
| Research integrity | 0.002 | 0.002 |
| Insufficient payload (model declined to judge) | 0.003 | 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".