Characterizing Urban-Rural Differences in Colon Cancer Outcomes
Bibliographic record
Abstract
OBJECTIVE: We aimed to explore possible drivers for urban-rural disparities in colon cancer outcomes in a single-payer health care system where all patients had access to universal health care coverage. METHODS: Patients diagnosed with stage II/III colon cancer between 2004 and 2015 in Alberta, Canada were reviewed. On the basis of postal code, patients were categorized as living in urban, rural, or suburban areas based on travel distance to the cancer center. Kaplan-Meier methods and Cox regression models assessed the associations among the area of residence, receipt of treatment, and overall survival (OS). RESULTS: Of 6163 patients identified, there were 3691, 1779, and 693 from urban, rural, and suburban areas, respectively. There was a larger proportion of younger patients (P=0.033) and left-sided colon cancers (P=0.042) in urban areas. Urban patients experienced shorter times from diagnosis to surgery (P<0.001), but longer delays from surgery to adjuvant chemotherapy (P=0.001). A significant difference in outcomes was identified among urban, rural, and suburban populations where median OS were 104, 94, and 83 months, respectively (P<0.001). In multivariate analysis, the location of residence continued to predict for worse OS in suburban (hazard ratio=1.60, 95% confidence interval: 1.24-2.07, P<0.001) and rural areas (hazard ratio=1.24, 95% confidence interval: 1.02-1.50, P=0.042), when compared with urban areas. CONCLUSIONS: In this population-based study, urban-rural differences in colon cancer survival persist, even in settings with universal health care coverage. These findings may be partly driven by a younger population with more left-sided colon cancers as well as expedited surgical intervention in urban populations, but these factors do not fully explain the disparities.
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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 | 0.002 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.002 | 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 | 0.001 |
| 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".