Occult tumor burden contributes to racial disparities in stage‐specific colorectal cancer outcomes
Bibliographic record
Abstract
BACKGROUND: There are differences in outcomes in blacks compared with whites with lymph node-negative (pN0) colorectal cancer. Recurrence in pN0 patients suggests the presence of occult metastases undetected by conventional approaches. This study explores the association of racial differences in outcomes with occult tumor burden in regional lymph nodes. METHODS: Lymph nodes (range, 2-159) from 282 prospectively enrolled pN0 colorectal cancer patients followed for a median of 24 months (range, 2-63 months) were subjected to molecular analysis. Occult tumor burden was estimated by quantifying the expression of GUCY2C, a biomarker for metastatic colorectal cancer cells. Risk categories defined using occult tumor burden was the primary outcome measure. Association of prognostic variables and risk were defined by multivariate polytomous logistic regression. RESULTS: Occult tumor burden stratified this cohort of 259 whites and 23 blacks into categories with low (60%; recurrence rate [RR] = 2.3%; 95% confidence interval [CI], 0.1%-4.5%), intermediate (31%; RR = 33.3%; 95% CI, 23.7%-44.1%), and high (9%; RR = 68.0%; 95% CI, 46.5%-85.1%; P < .001) risk. Blacks compared with whites exhibited 4-fold greater occult metastases in individual lymph nodes (P < .001). Multivariate analysis revealed that race (P = .02), T stage (P = .02), and number of lymph nodes collected (P = .003) were independent prognostic markers of risk category. Blacks compared with whites were more likely to harbor levels of occult tumor burden, associated with the highest recurrence risk (adjusted odds ratio = 5.08; 95% CI, 1.69-21.39; P = .007). CONCLUSIONS: Racial disparities in stage-specific outcomes in colorectal cancer are associated with differences in occult tumor burden in regional lymph nodes.
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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 | 0.000 |
| 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) | 0.002 | 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".