Impact of Geography on Care Delivery and Survival for Noncurable Pancreatic Adenocarcinoma: A Population-Based Analysis
Bibliographic record
Abstract
BACKGROUND: Little is known about how the geographic distribution of cancer services may influence disparities in outcomes for noncurable pancreatic adenocarcinoma. We therefore examined the geographic distribution of outcomes for this disease in relation to distance to cancer centers. METHODS: We conducted a retrospective population-based analysis of adults in Ontario, Canada, diagnosed with noncurable pancreatic adenocarcinoma from 2004 through 2017 using linked administrative healthcare datasets. The exposure was distance from place of residence to the nearest cancer center providing medical oncology assessment and systemic therapy. Outcomes were medical oncology consultation, receipt of cancer-directed therapy, and overall survival. We examined the relationship between distance and outcomes using adjusted multivariable regression models. RESULTS: Of 15,970 patients surviving a median of 3.3 months, 65.6% consulted medical oncology and 38.5% received systemic therapy. Regions with comparable outcomes were clustered throughout Ontario. Mapping revealed regional discordances between outcomes. Increasing distance (reference, ≤10 km) was independently associated with lower likelihood of medical oncology consultation (relative risks [95% CI] for 11-50, 51-100, and ≥101 km were 0.90 [0.83-0.98], 0.78 [0.62-0.99], and 0.77 [0.55-1.08], respectively) and worse survival (hazard ratios [95% CI] for 11-50, 51-100, and ≥101 km were 1.08 [1.04-1.12], 1.17 [1.10-1.25], and 1.10 [1.02-1.18], respectively), but not with likelihood of receiving therapy. Receipt of therapy seems less sensitive to distance, suggesting that distance limits entry into the cancer care system via oncology consultation. Regional outcome discordances suggest inefficiencies within and protective factors outside of the cancer care system. CONCLUSIONS: These findings provide a basis for clinicians to optimize their practices for patients with noncurable pancreatic adenocarcinoma, for future studies investigating geographic barriers to care, and for regional interventions to improve access.
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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 | 0.003 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.002 |
| Bibliometrics | 0.001 | 0.003 |
| Science and technology studies | 0.001 | 0.001 |
| Scholarly communication | 0.001 | 0.000 |
| Open science | 0.001 | 0.001 |
| Research integrity | 0.000 | 0.001 |
| 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".