Stage and cancer‐specific mortality differ within specific Asian ethnic groups for upper tract urothelial carcinoma: North American population‐based study
Bibliographic record
Abstract
OBJECTIVES: To examine the effect of specific Asian ethnic subgroups on stage at presentation and cancer-specific mortality in non-metastatic upper tract urothelial carcinoma among North American upper tract urothelial carcinoma Asian patients treated with radical nephroureterectomy. METHODS: We relied on the Surveillance, Epidemiology and End Results database, from 2004 to 2016. Kaplan-Meier plots and multivariable Cox regression models predicting cancer-specific mortality were used. RESULTS: (25.0% and 18.5%, respectively), relative to other Asian ethnic subgroups. In Kaplan-Meier plots, Vietnamese patients showed the highest cancer-specific mortality rate. In multivariable models, Vietnamese ethnicity also independently predicted higher cancer-specific mortality (hazard ratio 2.15, P = 0.02 and hazard ratio 1.96, P = 0.03), relative to Japanese and Chinese patients. All other Asian ethnic subgroups showed similar cancer-specific mortality patterns. CONCLUSION: Vietnamese and Chinese patients are at a stage disadvantage at upper tract urothelial carcinoma diagnosis, relative to all other Asian ethnicities. After adjustment for stage, only Vietnamese patients showed a survival disadvantage relative to all other Asian ethnic subgroups. As a result, it appears that Vietnamese patients not only present at a higher upper tract urothelial carcinoma stage, but additionally appear to harbor upper tract urothelial carcinoma that progresses at a faster rate than in other Asian ethnic subgroups.
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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 | 0.001 | 0.001 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.000 | 0.001 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.000 | 0.001 |
| Open science | 0.000 | 0.000 |
| Research integrity | 0.000 | 0.000 |
| 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".