Robot-assisted laparoscopic partial nephrectomy: Early single Canadian institution experience
Bibliographic record
Abstract
BACKGROUND: Although robot-assisted partial nephrectomy (RALPN) has been increasingly adopted, open procedures continue to be the reference nephron-sparing technique. We describe our initial surgical outcomes of RALPN in our single institution robotic program. METHODS: Between January 2011 and February 2013, 65 consecutive patients underwent a RALPN by 2 surgeons. Preoperative characteristics, including the R.E.N.A.L. nephrometry score, perioperative parameters, and postoperative course, including renal function, were assessed from a retrospective database. The mean follow-up was 12 months. RESULTS: The mean age was 60.2 years and the mean tumour size was 3.9 cm. According to the R.E.N.A.L. nephrometry score, the tumours were classified moderately and highly complex tumours in 51% and 18.5% of cases, respectively. Median warm ischemia time (WIT) was 21 minutes. Factors associated with WIT were R.E.N.A.L. nephrometry score, tumour size, complication rates and surgeon experience. No conversion or grade 4 to 5 complications were reported. The mean hospital stay was 3 days. The overall complication rate was 24.6% (re-admission rate 7.7%), and decreased to 12% after 20 cases. After these initial 20 cases, a trifecta rate (no margins, preserved renal function, no complications) of 64.3% was achieved in moderately and highly complex tumours. The mean change in estimated glomerular filtration rate was 6.7 mL/min without severe postoperative renal failure. INTERPRETATION: RALPN is a safe and feasible procedure with low specific morbidity, even in moderately or highly complex renal masses. The WIT depends on tumour characteristics, mainly determined by the R.E.N.A.L. nephrometry score and is improved by surgeon experience. Longer follow-up is needed to assess the oncologic mid-term safety of the procedure.
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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 | 0.000 | 0.002 |
| Meta-epidemiology (narrow) | 0.001 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.001 | 0.002 |
| Science and technology studies | 0.001 | 0.001 |
| Scholarly communication | 0.001 | 0.000 |
| Open science | 0.001 | 0.001 |
| Research integrity | 0.000 | 0.001 |
| 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".