Racial differences in the outcome of patients with urothelial carcinoma of the upper urinary tract: an international study
Bibliographic record
Abstract
Study Type – Therapy (case series) Level of Evidence 4 What’s known on the subject? and What does the study add? The differential effect of ethnicity on clinico‐pathological features and cancer‐related outcomes remains uninvestigated in upper tract urothelial carcinoma. Ethnicity was not an independent predictor of either recurrence or cancer‐related death in upper tract urothelial carcinoma. OBJECTIVE • To assess the impact of differences in ethnicity on clinico‐pathological characteristics and outcomes of patients with upper urinary tract urothelial carcinoma (UTUC) in a large multi‐center series of patients treated with radical nephroureterectomy (RNU). MATERIALS AND METHODS • We retrospectively collected the data of 2163 patients treated with RNU at 20 academic centres in America, Asia, and Europe. • Univariable and multivariable Cox regression models addressed recurrence‐free survival (RFS) and cancer‐specific survival (CSS). RESULTS • In all, 1794 (83%) patients were Caucasian and 369 (17%) were Japanese. All the main clinical and pathological features were significantly different between the two ethnicities. • The median follow‐up of the whole cohort was 36 months. At last follow‐up, 554 patients (26%) developed disease recurrence and 461 (21%) were dead from UTUC. • The 5‐year RFS and CSS estimates were 71.5% and 74.2%, respectively, for Caucasian patients compared with 68.8% and 75.4%, respectively, for Japanese patients. • On univariable Cox regression analyses, ethnicity was not significantly associated with either RFS ( P = 0.231) or CSS ( P = 0.752). • On multivariable Cox regression analyses that adjusted for the effects of age, gender, surgical type, T stage, grade, tumour architecture, presence of concomitant carcinoma in situ , lymphovascular invasion, tumour necrosis, and lymph node status, ethnicity was not associated with either RFS (hazard ratio [HR] 1.1; P = 0.447) or CSS (HR 1.0; P = 0.908). CONCLUSIONS • There were major differences in the clinico‐pathological characteristics of Caucasian and Japanese patients. • However, RFS and CSS probabilities were not affected by ethnicity and race was not an independent predictor of either recurrence or cancer‐related death.
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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 | 0.002 | 0.004 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.001 | 0.001 |
| Bibliometrics | 0.001 | 0.002 |
| Science and technology studies | 0.001 | 0.001 |
| Scholarly communication | 0.001 | 0.001 |
| Open science | 0.000 | 0.001 |
| Research integrity | 0.001 | 0.000 |
| 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".