Total robotic surgical volume influences outcomes of low-volume robotic-assisted partial nephrectomy over an extended duration
Bibliographic record
Abstract
INTRODUCTION: The objective of this study was to examine the surgeon's experience of low-volume robotic-assisted partial nephrectomy (RAPN) over an extended duration, and whether a high-volume fellowship training influenced the outcomes. METHODS: Data on all RAPN at a tertiary center performed by a uro-oncologist were retrospectively collected. The surgeon experience was assessed by examining perioperative outcomes among three groups of consecutive patients (first=14, second=14, third=15 patients, respectively). RESULTS: Between February 2014 and February 2020, 45 RAPNs were performed out of a total of 200 robotic procedures. The median tumor size was 3 cm, and 28 (65%) patients had a R.E.N.A.L nephrometry score (RNS) ≥7. The median operative time and warm ischemia time (WIT) were 190 and 16 minutes, respectively. The median estimated blood loss (EBL) was 100 mL. Two (4%) patients had a positive surgical margin (PSM). Overall, five (12%) complications were recorded. All except one were minor (Clavien I-II). The median followup was 26.2 months. Trifecta and pentafecta were achieved in 40 (93%) and 27 (81.8%) patients, respectively. Increased surgeon experience was significantly associated with a shorter operative time and less EBL. Furthermore, there was an independent association between surgeon experience and operative time and EBL, and between RNS and operative time and WIT. CONCLUSIONS: With fellowship training and subsequent adequate total number of robotic procedures during practice, it is possible to perform RAPN with favorable perioperative outcomes in the setting of low-volume of cases over an extended duration.
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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 | 0.004 |
| 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) | 0.002 | 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".