African American vs Caucasian race/ethnicity in adrenocortical carcinoma patients
Bibliographic record
Abstract
In some primaries, African American race/ethnicity predisposes to higher stage and worse survival. We tested for differences in cancer-specific mortality (CSM) and other-cause mortality (OCM) in patients with adrenocortical carcinoma (ACC) according to African American vs Caucasian race/ethnicity. We hypothesized that African Americans present with higher tumor stage and grade, do not receive the same treatment, and experience worse oncological outcomes than Caucasians. Within Surveillance, Epidemiology, and End Results database, we identified 1016 ACC patients: 123 (12.1%) African Americans vs 893 (87.9%) Caucasians. Propensity score matching (PSM) (age, sex, marital status, grade, T, N, and M stages, and treatment type), Poisson-smoothed cumulative incidence plots, and competing risk regression (CRR) were used. Compared to Caucasians, African Americans were more frequently unmarried (56.9% vs 35.5%, P < 0.001). No clinically meaningful or statistically significant differences were observed for age, grade, T, N, and M stages, as well as treatment type (all P > 0.05). After PSM (1:4), 123 African Americans and 492 Caucasians remained and were included in CRR analysis. In multivariable CRR models, CSM and OCM rates were not different between the two race/ethnicities (hazard ratio: 0.84, P = 0.3). In African Americans, 5-year CSM rates were 31.2% and 75.3% in European Network for the Study of Adrenal Tumors (ENSAT) stages I-II and III-IV, respectively vs 32.9% and 75.4% in Caucasians. Overall 5-year OCM rates were 11.0% vs 10.1% in respectively African Americans and Caucasians. Unlike other primaries, in ACC, African American race/ethnicity is not associated with higher disease stage at initial diagnosis or worse survival.
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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 | 0.000 |
| Bibliometrics | 0.000 | 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".