Assessment of the VENUSS and GRANT Models for Individual Prediction of Cancer-specific Survival in Surgically Treated Nonmetastatic Papillary Renal Cell Carcinoma
Bibliographic record
Abstract
Background: Guidelines recommend VENUSS and GRANT models for the prediction of cancer control outcomes after nephrectomy for nonmetastatic papillary renal cell carcinoma (pRCC). Objective: To test the ability of VENUSS and GRANT models to predict 5-yr cancer-specific survival in a North American population. Design setting and participants: For this retrospective study, we identified 4184 patients with unilateral surgically treated nonmetastatic pRCC in the Surveillance, Epidemiology, and End Results database (2004-2019). Outcome measurements and statistical analysis: The original VENUSS and GRANT risk categories were applied to predict 5-yr cancer-specific survival. A cross-validation method was used to test the accuracy and calibration of the models and to conduct decision curve analyses for the study cohort. Results and limitations: The VENUSS and GRANT categories represented independent predictors of cancer-specific mortality. On cross-validation, the accuracy of the VENUSS and GRANT risk categories was 0.73 and 0.65, respectively. Both models showed good calibration and performed better than random predictions in decision curve analysis. Limitations include the retrospective nature of the study and the absence of a central pathological review. Conclusion: VENUSS risk categories fulfilled prognostic model criteria for predicting cancer-specific survival 5 yr after surgery in North American patients with nonmetastatic pRCC as recommended by guidelines. Conversely, GRANT risk categories did not. Thus, VENUSS risk categories represent an important tool for counseling, follow-up planning, and patient selection for appropriate adjuvant trials in pRCC. Patient summary: We tested the ability of two validated methods (VENUSS and GRANT) to predict death due to papillary kidney cancer in a North American population. The VENUSS risk categories showed good performance in predicting 5-year cancer-specific survival.
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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 | 0.021 | 0.055 |
| Meta-epidemiology (narrow) | 0.001 | 0.000 |
| Meta-epidemiology (broad) | 0.001 | 0.002 |
| Bibliometrics | 0.002 | 0.001 |
| Science and technology studies | 0.000 | 0.001 |
| Scholarly communication | 0.001 | 0.001 |
| Open science | 0.002 | 0.002 |
| Research integrity | 0.001 | 0.001 |
| 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".