Renal nephrometry score and predictors of complications in partial nephrectomies.
Bibliographic record
Abstract
512 Background: Feasibility of partial nephrectomy for small renal masses extends beyond standard clinical tumor size. We analyze patient characteristics and anatomic tumor factors to determine variables associated with surgical complications after partial nephrectomy. Methods: Retrospective review of all patients who underwent partial nephrectomy at our institution between January 1, 2012 and Aug 31, 2013. Follow-up extended to 8 week post-operative outpatient clinic visit. The R.E.N.AL. Nephrometry score is a tumor descriptive (the maximum radius, exophytic/endophytic, nearness to collecting system/sinus, anterior/posterior position, location relative to polar line) that was applied to each pre-operative scan. Standardized grading systems and statistical analysis were applied. Results: Of the 83 patients who underwent partial nephrectomy 72 had a laparoscopic approach. Seventeen (20%) patients had complications and seven were Clavien-Dindo grade 3 to 4. Two patients had laparoscopic partial nephrectomies converted intra-operatively to radical nephrectomies; two other laparoscopic partial nephrectomies were converted to open partial nephrectomies. Forty-three (52%) of operated patients were either obese, morbidly obese, or super obese. Fifteen (18%) of patients had pathologic oncocytomas or angiomyelipomas. In univariate analysis Charlson comorbidity score (>6 p=0.0027), diabetes (42% p=0.0195), age (>70 p=0.02034), and total R.E.N.A.L. Nephrometry score (10-12, 67%, p=0.0254) were associated with complications. Nephrometry score also correlated with warm ischemic time (WIT) in laparoscopic cases (low 26 min [SD +/- 11.71], intermediate 31 min [SD +/- 7], high 34 min [SD +/- 14]). Conclusions: Categorizing renal masses according to the R.E.N.A.L. Nephrometry score may help us council patients towards expected WITs, complication rates, and predicted renal function outcomes. This is increasingly important as the majority of our patients are either obese, elderly, or have significant comorbidities; all of which have been shown to be associated with increased complication rates.
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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 | 0.003 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.001 | 0.001 |
| 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".