The association between the anatomical features of renal tumours and the functional outcomes of robot-assisted partial nephrectomy
Bibliographic record
Abstract
INTRODUCTON: We evaluated the the association between PADUA scores and postoperative renal function (after robot-assisted partial nephrectomy [RAPN]) and between PADUA scores and warm ischemic time (during RAPN). METHODS: We reviewed the clinical records of 106 patients who underwent RAPN for a single localized renal tumour between April 2009 and June 2012. Postoperative renal function was evaluated using estimated glomerular filtration rate (eGFR) in 85 patients who were followed for at least 6 months. PADUA scores for renal tumours were calculated using contrast-enhanced computed tomography images, if needed, along with magnetic resonance images in some cases. RESULTS: A PADUA score ≥10 and WIT ≥30 minutes were observed in 18 (17.0%) and 51 (48.1%) cases, respectively. PADUA scores were significantly correlated with WIT (p = 0.019) and percent change in eGFR at 6 months postoperatively (p = 0.005). PADUA score (continuous variable, odds ratio [OR] 1.694, p = 0.007) and the high-risk group (PADUA score ≥10) (OR 5.429; p = 0.020) were significantly associated with a WIT of ≥30 minutes by multivariate analysis. A 1-point increase in the PADUA score was associated with an eGFR decrease of >20% at 6 months after RAPN (OR 1.799; p = 0.076). In addition, a PADUA score ≥10, or high risk, (OR 13.965; p = 0.003) was an independent predictor of an eGFR decrease of >20% at 6 months after RAPN. CONCLUSIONS: The PADUA classification can reliably predict WIT and postoperative renal functional outcome after RAPN. Furthermore, the study suggests that anatomical aspects of renal tumours are associated with functional outcome after RAPN.
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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 | 0.001 | 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".