Vulnerability in Colorectal Cancer: Adjusted Gross Income and Geography as Factors in Determining Overall Survival in Colorectal Cancer: A Single-Center Study Across a Broad Income Inequality in an American Context
Bibliographic record
Abstract
Introduction: Regional differences in socioeconomic status (SES) are well known, and we believe that the use of geocoding (zip code) can facilitate the introduction of targeted interventions for underserved populations. This is a single-center, retrospective analysis of data extracted from the cancer registry at the Capital Health Cancer Center in Pennington, N. The Capital Health Cancer Center in central New Jersey primarily serves two counties, catering to a diverse patient population from a wide range of socioeconomic backgrounds. Methods: We abstracted 1269 consecutive cases of colorectal cancer (CRC) diagnosed and treated between 2000 and 2019 from the Cancer Registry of the Capital Health Cancer Center (CHCC). Using the definition of SES based on previously published work, and zip codes (geocoding), we created four SES levels. We stratified our subjects according to their stage at diagnosis, age at diagnosis, race, and ethnicity. The primary outcome variable was overall survival (OS). Results: There was a statistically significant difference in OS based on SES, with the highest overall survival (OS) in the high-SES group (47 months) and the shortest OS in the low and mid-low-SES groups (40.4 and 30 months, respectively). Subjects living in high-SES areas were predominantly white (88.2%) and diagnosed at a later age (mean of 68.9 years of age) compared to individuals who lived in a low-SES area, who were predominantly non-white (72.6%) and diagnosed somewhat earlier in life (65.1 years of age). White people were diagnosed later in life (70.9 years of age) compared to non-white populations, including Black (66.5), Asian (61.7), and Hispanic (58.5) (p = 0.001) populations, but this did not lead to a significant difference in OS (p = 0.56). Stage at diagnosis was a significant predictor of OS, but was unrelated to SES (p = 0.066). A Cox proportional hazard ratio (HR) model showed that the risk of dying from colorectal cancer decreases with a higher socioeconomic status (SES). Those from mid-high-SES backgrounds had a 19% lower risk (HR 0.81), and those from high-SES areas had a 45% lower risk (HR 0.55) compared to individuals from low-SES areas. Conclusions: The vulnerability of patients with CRC in central New Jersey is a complex issue, influenced by many different variables. Our results indicate that SES is the most critical factor affecting OS after being diagnosed with CRC.
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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 | 0.001 | 0.002 |
| Science and technology studies | 0.001 | 0.000 |
| Scholarly communication | 0.001 | 0.001 |
| Open science | 0.000 | 0.001 |
| 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".