Associations between radiographic characteristics and change in renal function following partial nephrectomy using 24-hour creatinine clearance
Bibliographic record
Abstract
BACKGROUND: Radiographic characteristics may be associated with the degree of renal function preservation following partial nephrectomy. The purpose of this study was to determine the impact of preoperative radiographic variables on change in renal function using 24-hour urine creatinine clearance (uCrCl). METHODS: Patients with partial nephrectomy performed from November 2003 to 2008 were enrolled in the study. Serum creatinine and 24-hour urine was collected preoperatively and at 3, 6 and 12 months postoperatively. Computed tomography or magnetic resonance imaging was used to determine tumour size, tumour location and renal volume. RESULTS: Of the 36 patients, median age was 62 (range 30-78) and 21 (58%) were male. The mean tumour diameter was 2.8±1.4 cm. Twenty-two (61%) tumours were located at the renal pole and 11 (31%) were endophytic. Overall, mean preoperative uCrCl was 88.8±34.2 mL/min and mean postoperative uCrCl was 82.8±33.6 mL/min (6.8%; p < 0.01). On multivariable analysis, no single characteristic was associated with a clinically prohibitive decrease in renal function (-9.4% if endophitic, p = 0.06; -0.57% per cm diameter, p = 0.73; and -6.9% if located at the renal pole, p = 0.15). The total renal volume was also not significantly associated with renal function change (-1.1% per 100 cc, p = 0.86).
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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) | 0.001 | 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".