Comparison of robotic and open partial nephrectomy: Single-surgeon matched cohort study
Bibliographic record
Abstract
INTRODUCTION: We present comparative outcomes among matched patients who underwent robotic partial nephrectomy (RPN) or open partial nephrectomy (OPN) by a single surgeon at a single institution. METHODS: We reviewed the medical records of 200 patients who underwent RPN (n = 100) or OPN (n = 100) between May 2003 and May 2013. The patients who underwent RPN were matched for age, gender, body mass index (BMI), American Society of Anesthesiologists (ASA) score, as well as tumour size, side and location. Perioperative outcomes were compared. RESULTS: There was no significant difference between the 2 cohorts with respect to patient age, BMI, ASA score, preoperative glomerular filtration rate, tumour size and the R.E.N.A.L. nephrometry score. The mean operative time was longer in the RPN group, but there were no significant differences with respect to warm ischemic time and postoperative renal function. The length of hospitalization and use of postoperative analgesics (ketoprofen) were more favourable in the RPN cohort. There was no significant difference in the mean estimated blood loss, transfusion rate, or complications between the cohorts. CONCLUSIONS: Considering the perioperative and postoperative parameters, RPN is a viable option as a nephron-sparing surgical procedure for small renal masses that yields outcomes comparable to those achieved with OPN. Despite matched cohort analysis among patients who underwent PN by a single surgeon, there may be inherent selection bias; therefore future prospective trials are needed.
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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 | 0.002 | 0.004 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.001 | 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) | 0.002 | 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".