Racial disparities and socioeconomic status in men diagnosed with testicular germ cell tumors
Bibliographic record
Abstract
BACKGROUND: Previous reports indicated that African-American men with testicular germ cell tumors (TGCTs) have more aggressive tumor characteristics and less favorable outcomes than other men. The authors of this report evaluated the effects of race and socioeconomic status (SES) on stage distribution, overall mortality (OM), and cancer-specific mortality (CSM) in men with TGCTs. METHODS: The Surveillance, Epidemiology, and End Results (SEER) database was used to identify 22,553 men who were diagnosed with TGCTs between 1988 and 2006. Kaplan-Meier and Cox regression analyses were generated to predict OM and CSM. Covariates of the analyses included race, SES, age, histologic subtype, disease stage, procedure type, SEER registry, and year of diagnosis. The interaction between race and SES also was examined. RESULTS: Overall, there were 516 African-American men, 21,090 Caucasian men, and 947 men of other races. African-Americans (14.9%) and individuals with low SES (10.7%) had a higher proportion of distant stage disease. CSM and OM rates were significantly higher for African-American patients and for patients who resided in low SES counties. Multivariate analyses revealed that African-American men and men with low SES were more likely to die of OM and CSM relative to Caucasian men (P < .001) and men with high SES (P < .001), respectively. The interaction between race and SES was not significant. CONCLUSIONS: African-American race and low SES appeared to predispose men to more advanced disease stages and to higher OM and CSM rates. These observations may warrant race-specific and/or SES-specific adjustments in the treatment of TGCT.
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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 | 0.000 | 0.002 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.001 | 0.001 |
| 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) | 0.002 | 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".