Split Renal Function Is Fundamentally Important for Predicting Functional Recovery After Radical Nephrectomy
Why this work is in the frame
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Bibliographic record
Abstract
While partial nephrectomy (PN) is generally preferred for localized renal cell carcinoma (RCC), radical nephrectomy (RN) is occasionally required. A new-baseline glomerular filtration rate (NBGFR) >45 ml/min/1.73 m2 after kidney cancer surgery is associated with strong survival outcomes. If NBGFR after RN will be above this threshold and the tumor has increased oncologic potential, RN may be a relevant consideration. Predicting NBGFR, defined as the GFR at 3–12 mo after RN, has been challenging owing to omission of two important parameters: split renal function (SRF) and renal function compensation (RFC). Our objective was to evaluate a simple SRF-based model in comparison to five published non–SRF-based models using data from a retrospective cohort of 445 RN patients. SRF was obtained via readily available semiautomated software (FUJIFILM Medical Systems) that provides differential parenchymal volume analysis on the basis of preoperative imaging. Our conceptually simple and clinically implementable SRF-based model more accurately predicts NBGFR after RN than five published non–SRF-based models (all p < 0.01). The SRF-based model also improved prediction of the clinically relevant threshold of NBGFR >45 ml/min/1.73 m2 (all p < 0.05). We validated a novel approach for more accurate prediction of kidney function after removal of one kidney. Our approach can be used in clinical and practice and will help in making decisions on full or partial removal of a kidney for kidney cancer.
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Full frame distilled prediction
Teacher imitationNot calibrated prevalence, not ground truth. Human validation pending. Learned from the 10,348 direct Codex labels and 10,348 direct Gemma labels. Candidate is the union of thresholded teacher heads; consensus is their intersection. These outputs are machine_predicted_unvalidated and are not human labels or direct frontier model labels.
Codex and Gemma teacher scores by category
| Category | Codex | Gemma |
|---|---|---|
| Metaresearch | 0.003 | 0.000 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.000 | 0.000 |
| Science and technology studies | 0.001 | 0.001 |
| Scholarly communication | 0.000 | 0.000 |
| Open science | 0.001 | 0.001 |
| Research integrity | 0.000 | 0.000 |
| Insufficient payload (model declined to judge) | 0.003 | 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 it