Association between R.E.N.A.L nephrometry score and perioperative outcomes following open partial nephrectomy under cold ischemia
Bibliographic record
Abstract
INTRODUCTION: We investigate the clinical significance of the R.E.N.A.L. nephrometry score for renal neoplasm following open partial nephrectomy (PN) under cold ischemia. METHODS: A retrospective analysis was conducted using clinical data of 98 consecutive patients with clear cell renal cell carcinoma who underwent open PN by a single surgeon from December 2000 to September 2012. Tumour complexity was stratified into 3 categories: low (4-6), moderate (7-9) and high (10-12) complexity. Perioperative outcomes, such as complications, cold ischemic time, estimated blood loss and renal function, were analyzed according to the complexity by NS. Complications were stratified using the Clavien-Dindo classification system. RESULTS: Tumour complexity according to nephrometry score was assessed as low in 16 (16.3%), moderate in 48 (49.0%) and high in 34 (34.7%). The median cold ischemic time did not differ significantly among the 3 groups (36.0 minutes in low-, 40 minutes in moderate- and 43 minutes in the high-complexity group, p = 0.421). Total complications did not differ significantly (2 (2.0%) in low, 4 (4.1%) in moderate and 4 (4.1%) in high, p = 0.984). Each Grade 3 complication occurred in the moderate (urine leakage) and high groups (lymphocele). Postoperative renal functional outcomes were similar among the groups (p = 0.729). Only mean estimated blood loss was significantly different with nephrometry score (p = 0.049). CONCLUSIONS: The nephrometry score, as used in an open PN series under cold ischemia, was not significantly associated with perioperative outcomes (i.e., ischemia time, complications, renal functional preservation).
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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 | 0.001 |
| 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".