Ipsilateral renal function preservation following minimally invasive partial nephrectomy: The effect of tumour characteristics and warm ischemic time
Bibliographic record
Abstract
INTRODUCTION: The relative impact of preoperative and perioperative variables on renal function following partial nephrectomy (PN) is controversial. To further investigate, we assess the effects of tumour complexity, warm ischemic time (WIT), and volume of resected renal parenchyma on ipsilateral renal function (IRF) outcomes following minimally invasive PN. METHODS: Of patients who underwent laparoscopic or robotic-assisted PN between 2002 and 2011 at our institution, 99 met our inclusion criteria. The effects of preoperative tumour complexity (using RENAL nephrometry score), perioperative WIT, and pathological tumour volumes on ipsilateral renal function preservation (%IRF) were analyzed. %IRF was defined as the proportion of postoperative to preoperative ipsilateral renal function calculated using MAG3 nuclear renography. RESULTS: Increasing RENAL nephrometry score (RNS) and WIT were independently predictive of inferior %IRF at 6-12-week postoperative followup in univariate and multivariate analyses. Of RNS properties, masses that were endophytic, near the collecting system, or central in location were associated with inferior %IRF, with nearness to collecting system as the strongest predictor; however, RNS was no longer predictive of %IRF in cases requiring more than 30 minutes of WIT. CONCLUSIONS: In renal masses amenable to resection by minimally invasive PN, longer WIT was the most important predictor of inferior %IRF. Although increasing RNS score influenced %IRF, the overall clinical significance of RNS is limited and should not influence operative decision-making in efforts to preserve renal function. Furthermore, small volumes of renal parenchyma can be safely resected without impairment of long-term IRF.
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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 | 0.001 | 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".