Hispanic<i>vs.</i>Caucasian Race/Ethnicity in Adrenocortical Carcinoma Patients
Bibliographic record
Abstract
BACKGROUND/AIM: In primaries other than adrenocortical carcinoma (ACC), Hispanic race/ethnicity may predispose to higher stage at initial diagnosis and may result in worse survival. We tested the association between Hispanic race/ethnicity and cancer specific mortality (CSM) in ACC patients in addition to testing for differences in other-cause mortality (OCM) rates between Hispanics and Caucasians. PATIENTS AND METHODS: Within Surveillance, Epidemiology, and End Results database (2004-2018), we identified 1,060 ACC patients: 167 (15.8%) Hispanics vs. 893 (84.2%) Caucasians. Propensity score matching (age, sex, grade, T, N and M stages, treatment types), cumulative incidence plots Poisson-smoothing and competing risk regression (CRR) were used. RESULTS: Compared to Caucasians, Hispanics were younger (51 vs. 57 years, p<0.001) and presented higher rates of T3-4 primary tumor stage (52.7% vs. 42.8%, p=0.007). No other statistically significant differences were observed for grade, lymph node invasion, distant metastases, European Network for the Study of Adrenal Tumors (ENSAT) stage and treatment type (p>0.05 in all cases). After matching (1:3), 167 Hispanics and 501 Caucasians remained and were included in CRR analyses. In Hispanics, five-year CSM rates were 38.0% and 78.8% in respectively ENSAT stages I-II and III-IV vs. 34.1% and 74.4% in Caucasians. Overall, five-year OCM rates were 10.7% vs. 9.0% in Hispanics and Caucasians, respectively. In multivariable CRR models, Hispanic race/ethnicity was not an independent predictor for higher CSM (hazard ratio=1.18, p=0.2). CONCLUSION: In ACC, relative to Caucasians, Hispanic race/ethnicity is associated with lower age at initial diagnosis, but not with higher tumor stage or survival disadvantage.
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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 | 0.001 | 0.000 |
| Open science | 0.000 | 0.000 |
| Research integrity | 0.000 | 0.000 |
| Insufficient payload (model declined to judge) | 0.003 | 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".