Quality and Quantity in Kidney Cancer Surgery
Bibliographic record
Abstract
Objectives: To model renal function 2 years following radical nephrectomy with quantitative analyses using clinical, histopathologic, and renal composite cortical volumes (CCV). Methods: This retrospective study involved an assessment of the nonneoplastic kidney tissue by three blinded nephropathologists using modified Banff 1997 criteria for renal allograft pathology. Volumetric image acquisition was obtained by three independent radiologists using preoperative imaging. A 2-year estimated glomerular filtration (eGFR) calculator was created. Results: Among the 126 patients, median age was 60 years; median CCV, 398.1 cm3; preoperative eGFR, 77 mL/min/1.73 m2; and 2-year postoperative eGFR, 54 mL/min/1.73 m2. Of the subjects, 64% had hypertension, 26% diabetes, and 37% were smokers. Increasing age, glomerulopathy/sclerosis, tubulointerstitial scarring, and arteriosclerosis were statistically significantly and adversely associated with eGFR. Conversely, increasing CCV was associated with a higher eGFR. Conclusions: Quantitative analysis of the nephrectomized kidney in conjunction with patient age can accurately predict renal function at 2 years.
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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 | 0.003 | 0.010 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.001 |
| Bibliometrics | 0.001 | 0.001 |
| Science and technology studies | 0.000 | 0.001 |
| Scholarly communication | 0.001 | 0.001 |
| Open science | 0.001 | 0.001 |
| 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".