Early-Onset Colorectal Cancer Survival Differences and Potential Geographic Determinants Among Men and Women in Utah
Bibliographic record
Abstract
By 2030, early-onset colorectal cancer (EOCRC) is expected to become the leading cancer-related cause of death for people age 20 to 49. To improve understanding of this phenomenon, we analyzed the geographic determinants of EOCRC in Utah by examining county-level incidence and mortality. We linked data from the Utah Population Database to the Utah Cancer Registry to identify residents (age 18-49) diagnosed with EOCRC between 2000 and 2020, and we used spatial empirical Bayes smoothing to determine county-level hotspots. We identified 1,867 EOCRC diagnoses (52.7% in male patients, 69.2% in non-Hispanic White patients). Ten counties (34%) were classified as hotspots, with high EOCRC incidence or mortality. Hotspot status was unrelated to incidence rates, but non-Hispanic ethnic-minority men (incidence rate ratio, 1.49; 95% CI, 1.15-1.91), Hispanic White men and women (incidence rate ratio, 2.24; 95% CI, 2.00-2.51), and Hispanic ethnic-minority men and women (incidence rate ratio, 4.59; 95% CI, 3.50-5.91) were more likely to be diagnosed with EOCRC. After adjustment for income and obesity, adults living in hotspots had a 31% higher hazard for death (HR, 1.31; 95% CI, 1.02-1.69). Survival was poorest for adults with a late-stage diagnosis living in hotspots (chi square (1) = 4.0; p = .045). Adults who were married or who had a life partner had a lower hazard for death than single adults (HR, 0.73; 95% CI, 0.58-0.92). The risk for EOCRC is elevated in 34% of Utah counties, warranting future research and interventions aimed at increasing screening and survival in the population age 18 to 49.
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How this classification was reachedexpand
Full frame distilled prediction
Teacher imitationNot calibrated prevalence, not ground truth. Human validation pending. Learned from the 10,348 direct Codex labels and 10,348 direct Gemma labels. Candidate is the union of thresholded teacher heads; consensus is their intersection. These outputs are machine_predicted_unvalidated and are not human labels or direct frontier model labels.
Codex and Gemma teacher scores by category
| Category | Codex | Gemma |
|---|---|---|
| Metaresearch | 0.001 | 0.000 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.001 | 0.000 |
| Bibliometrics | 0.000 | 0.000 |
| Science and technology studies | 0.000 | 0.002 |
| Scholarly communication | 0.000 | 0.000 |
| Open science | 0.000 | 0.000 |
| Research integrity | 0.000 | 0.000 |
| Insufficient payload (model declined to judge) | 0.000 | 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 teacher head, 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".