O6-2.6 Bladder cancer survival disparities in the United States: results from SEER-Medicare
Bibliographic record
Abstract
Introduction Black patients have lower bladder cancer survival rates than White patients, but previous studies have not been able to explain this difference. Recent work has found that racial disparities in bladder cancer survival persist after adjusting for sex, age, and tumour characteristics. The objective of this study was to assess the association of insurance status, comorbidities, marital status, the receipt of radical cystectomy and being qualified for federal assistance (a marker for low individual-level SES) with disparities in bladder cancer survival. Methods We identified 15 666 (592 Black and 15 074 White) bladder cancer cases diagnosed between 1992 and 1999 (follow-up through 2003) from the SEER-Medicare database, and constructed relative survival models to assess 5-year survival disparities. Results The relative survival ratios (RSR) for Black patients vs White patients were as follows: unadjusted, RSR 2.22 (95% CI 1.90 to 2.59), adjusting for year of diagnosis, registry, age, sex, stage, and grade, RSR 1.54 (95% CI 1.32 to 1.81); additionally adjusting for comorbidity score, marital status, and receipt of radical cystectomy, RSR 1.45 (95% CI 1.25 to 1.70); additionally adjusting for SES, RSR 1.26 (95% CI 1.08 to 1.48). Lower comorbidity score, being married, higher SES and receipt of radical cystectomy were independently associated with increased bladder cancer survival. Conclusions Racial disparities persist after adjusting for comorbidity score, marital status, receipt of radical cystectomy and SES in this insured population. As the SES variable used in this study does not capture the diversity across the SES gradient and it is strongly associated with race, further studies investigating the influence of SES and its correlates may provide further explication of this disparity.
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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 | 0.004 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.001 | 0.002 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.000 | 0.000 |
| Open science | 0.000 | 0.001 |
| Research integrity | 0.000 | 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".