Rural‐urban disparities in incidence and outcomes of neuroendocrine tumors: A population‐based analysis of 6271 cases
Bibliographic record
Abstract
BACKGROUND: Despite their rising incidence, neuroendocrine tumors (NETs) remain a poorly understood disease. Living in a rural area (RA) affects the incidence and outcomes of other types of cancer. This study compared the incidence and outcomes of NETs for patients in RAs and patients in urban areas (UAs). METHODS: A population-based cohort study of patients with NETs in Ontario, Canada from 1994 to 2011 was conducted. An RA was defined as any community with a population < 10,000 and outside the commuting zone of a metropolitan area. Incidence, advanced stage at presentation, distant recurrence-free survival (dRFS), and overall survival (OS) were compared between patients who lived in RAs and patients who lived in UAs with univariate and multivariate regression analyses. RESULTS: The cohort included 6271 patients diagnosed with NETs, of whom 13.5% (n = 846) resided in RAs. The incidence of NETs was higher in RAs at 3.01 per 100,000 per year versus UAs at 2.82 per 100,000 per year (relative rate, 1.10; P = .04). RA living was not associated with an advanced stage at presentation (odds ratio, 1.15; 95% confidence interval, 0.96-1.38). Patients who lived in RAs had worse 10-year dRFS (62.8% vs 65.9%, P = .03) and OS (44.6% vs 48.8%, P = .004). RAs were independently associated with decreased OS (hazard ratio, 1.16; 95% confidence interval, 1.04-1.30). CONCLUSIONS: Patients are more commonly diagnosed with NETs in RAs, but they do not present at more advanced stages in comparison with patients diagnosed in UAs. Patients living in RAs experience worse cancer recurrence and OS, and this is possibly related to variations in socioeconomic status, rural environmental factors, and access to specialized health care.
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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 | 0.000 | 0.001 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.001 | 0.000 |
| Bibliometrics | 0.001 | 0.001 |
| 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) | 0.000 | 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".