Highly predictive survival nomogram after upper urinary tract urothelial carcinoma
Bibliographic record
Abstract
BACKGROUND: Nephroureterectomy is the surgical standard of care for patients with upper urinary-tract urothelial carcinoma. The objectives of the current study were to identify the most informative predictors of cancer-specific mortality after nephroureterectomy, to devise an algorithm capable of predicting the individual probability of cancer-specific mortality, and to compare its prognostic accuracy to that of the International Union Against Cancer (UICC) staging system. METHODS: Within the Surveillance, Epidemiology, and End Results database, the authors identified 5918 patients who had been treated with nephroureterectomy. Within the development cohort (n=2959), multivariate Cox regression models predicting cancer-specific mortality were fitted by using age, stage, nodal status, sex, grade, race, type of surgery (nephroureterectomy with or without bladder-cuff removal), and tumor location (renal pelvis vs ureter). Backward variable elimination according to the Akaike information criterion identified the most accurate and parsimonious model. Model validation and calibration were performed within the external validation cohort (n=2959). External validation was also applied to the UICC staging system. RESULTS: The 5-year freedom from cancer-specific mortality rates in both the development and external validation cohorts was 77.3%. The most informative and parsimonious nomogram for cancer-specific-mortality-free survival relied on age, pT and pN stages, and tumor grade. In external validation, nomogram prediction of 5-year cancer-specific-mortality-free rate was 75.4% accurate and was significantly better (P<.001) than the UICC staging system (64.8%). CONCLUSIONS: The current nomogram is capable of predicting the prognosis in patients with upper urinary-tract urothelial carcinoma treated by nephroureterectomy with better accuracy than the UICC staging system. The authors recommend the application of this nomogram to routine clinical practice when counseling or making clinical decisions.
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How this classification was reachedexpand
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 | 0.000 | 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 | 0.000 | 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) | 0.003 | 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 itClassification
machine, unvalidatedMachine predicted; a candidate call from one teacher head, 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".