Outcomes in robot‐assisted partial nephrectomy for imperative vs elective indications
Bibliographic record
Abstract
OBJECTIVES: To assess and compare peri-operative outcomes of patients undergoing robot-assisted partial nephrectomy (RAPN) for imperative vs elective indications. PATIENT AND METHODS: We retrospectively reviewed a multinational database of 3802 adults who underwent RAPN for elective and imperative indications. Laparoscopic or open partial nephrectomy (PN) were excluded. Baseline data for age, gender, body mass index, American Society of Anaesthesiologists score and PADUA score were examined. Patients undergoing RAPN for an imperative indication were matched to those having surgery for an elective indication using propensity scores in a 1:3 ratio. Primary outcomes included organ ischaemic time, operating time, estimated blood loss (EBL), rate of blood transfusions, Clavien-Dindo complications, conversion to radical nephrectomy (RN) and positive surgical margin (PSM) status. RESULTS: After propensity-score matching for baseline variables, a total of 304 patients (76 imperative vs 228 elective indications) were included in the final analysis. No significant differences were found between groups for ischaemia time (19.9 vs 19.8 min; P = 0.94), operating time (186 vs 180 min; P = 0.55), EBL (217 vs 190 mL; P = 0.43), rate of blood transfusions (2.7% vs 3.7%; P = 0.51), or Clavien-Dindo complications (P = 0.31). A 38.6% (SD 47.9) decrease in Day-1 postoperative estimated glomerular filtration rate was observed in the imperative indication group and an 11.3% (SD 45.1) decrease was observed in the elective indication group (P < 0.005). There were no recorded cases of permanent or temporary dialysis. There were no conversions to RN in the imperative group, and seven conversions (5.6%) in the elective group (P = 0.69). PSMs were seen in 1.4% (1/76) of the imperative group and in 3.3% of the elective group (7/228; P = 0.69). CONCLUSION: We conclude that RAPN is feasible and safe for imperative indications and demonstrates similar outcomes to those achieved for elective indications.
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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 | 0.003 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| 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) | 0.001 | 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 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".