A Predictive Model for Kidney Failure After Nephrectomy for Localized Kidney Cancer: The Kidney Cancer Risk Equation
Bibliographic record
Abstract
Background: Nephrectomy is the mainstay treatment for individuals with localized kidney cancer. However, surgery can potentially result in functional kidney impairment leading to kidney failure requiring dialysis or transplantation. There are currently no clinical tools available for clinicians to pre-operatively identify which patients are at risk of kidney failure, and therefore, the aim of our study was to develop and validate a prediction equation for kidney failure after nephrectomy for kidney cancer. Methods: A population-level cohort study was conducted with adults (≥ 18 years old; n=1,026) from Manitoba, Canada who were diagnosed with non-metastatic kidney cancer between January 1, 2004 and December 31, 2016, were treated with either a partial or radical nephrectomy, and had at least 1 estimated glomerular filtration rate (eGFR) measurement available pre and post nephrectomy. Demographic, clinical, and laboratory data were used to develop the prediction models using Cox proportional hazards regression methods. We subsequently externally validated the models using data from 12,043 individuals from Ontario, Canada. The primary outcome was dialysis, transplantation, or an eGFR < 15 mL/min/1.73m2 during the follow-up period. Results: Among individuals in the development cohort (mean age 61.2 ± 11.7; mean eGFR 79.5 ± 22.8 mL/min/1.73m2; 39.0% partial nephrectomy/61.2% radical), 10.4% reached kidney failure during the follow-up period. The final model included 6 variables: age, sex, baseline eGFR, urine albumin-to-creatinine ratio, nephrectomy type, diabetes mellitus. The 5-year C-statistic was 0.83 (74.8, 91.4) in the development cohort and 0.86 (0.84, 0.88) in the validation cohort. Conclusions: We developed and externally validated a simple equation that incorporates easily accessible data and can accurately predict kidney failure in individuals undergoing nephrectomy for treatment of localized kidney cancer. This tool can help inform pre-operative discussion about kidney failure risk in patients facing surgical options for localized kidney cancer. Funding: Private Foundation Support
Fetched live from OpenAlex and de-inverted. Abstracts are not stored in this database: the inverted indexes are 8.6 GB of the frame’s 9.3 GB of text, and the host has 13 GB free.
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 | 0.004 | 0.007 |
| Meta-epidemiology (narrow) | 0.001 | 0.000 |
| Meta-epidemiology (broad) | 0.001 | 0.001 |
| Bibliometrics | 0.001 | 0.001 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.001 | 0.000 |
| Open science | 0.001 | 0.001 |
| Research integrity | 0.001 | 0.002 |
| 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".