Survival of stage <scp>III</scp> non‐seminoma testis cancer patients versus simulated controls, according to race/ethnicity
Bibliographic record
Abstract
BACKGROUND: It is unknown whether 5-year overall survival (OS) differs and to what extent between the American Joint Committee on Cancer stage III non-seminoma testicular germ cell tumor (NS-TGCT) patients and simulated age-matched male population-based controls, according to race/ethnicity groups. METHODS: We identified newly diagnosed (2004-2014) stage III NS-TGCT patients within the Surveillance Epidemiology and End Results database 2004-2019. For each case, we simulated an age-matched male control (Monte Carlo simulation), relying on Social Security Administration (SSA) Life Tables with 5 years of follow-up. We compared OS rates between stage III NS-TGCT patients and simulated age-matched male population-based controls, according to race/ethnicity groups (Caucasian, Hispanic, Asian/Pacific Islander and African American). Both, cancer-specific mortality (CSM) and other-cause mortality (OCM) were computed. RESULTS: Of 2054 stage III NS-TGCT patients, 60% were Caucasians versus 33% Hispanics versus 4% Asians/Pacific Islanders versus 3% African Americans. The 5-year OS difference between stage III NS-TGCT patients versus simulated age-matched male population-based controls was highest in Asians/Pacific Islanders (64 vs. 99%, Δ = 35%), followed by African Americans (66 vs. 97%, Δ = 31%), Hispanics (72 vs. 99%, Δ = 27%), and Caucasians (76 vs. 98%, Δ = 22%). The 5-year CSM rate was highest in Asians/Pacific Islanders (32%), followed by African Americans (26%), Hispanics (25%), and Caucasians (20%). The 5-year OCM rate was highest in African Americans (8%), followed by Caucasians (4%), Asians/Pacific Islanders (4%), and Hispanics (2%). CONCLUSION: Relative to SSA Life Tables, the highest 5-year OS disadvantage applied to stage III NS-TGCT Asian/Pacific Islander race/ethnicity group, followed by African American, Hispanic and Caucasian, in that order.
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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 | 0.002 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.001 |
| 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) | 0.001 | 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".