A population-based analysis of urban-rural disparities in advanced pancreatic cancer (APC) management and outcomes.
Bibliographic record
Abstract
e18049 Background: The high morbidity burden associated with APC means that its management is complex and frequently requires multidisciplinary care. Because of potential geographical barriers to healthcare access, we aimed to determine the effect of rurality on management and outcomes of APC patients. Methods: Patients diagnosed with APC (locally advanced or metastatic disease) from 2008 to 2015 and received gemcitabine (gem), gem plus nabpaclitaxel (gem/nab), or FOLFIRINOX at any 1 of 6 British Columbia cancer centers were reviewed. Using postal codes, the Google Maps Distance Matrix determined the distance from each patient’s residence to the closest cancer center. Rural and urban status were defined as patients living > / = 100 km and < 100 km to the closest treatment site, respectively. Different cut points were used in sensitivity analyses. Patients were also stratified according to Canadian census population sizes. Univariate and Cox regression analyses were applied to examine whether rurality resulted in variations in management and outcomes. Results: In total, we identified 659 patients: median age 68 years, 54.3% men, and 45.7% metastatic disease. Among them, 19.3% lived rurally. For treatment, 67.7%, 9.2%, and 23.1% received gem, gem/nab, and FOLFIRINOX, respectively. There were no differences in baseline clinical characteristics between rural and urban patients (all p > 0.05). Time from diagnosis to oncology appointment and time from appointment to treatment were 31.5 and 29.5 days for rural patients and 28.6 and 40.1 days for urban patients, respectively (all p > 0.05). In multivariate Cox regression, risk of death was similar between rural and urban groups (HR 0.864, 95% CI 0.619-1.206, p = 0.390). Furthermore, regression analysis found that population size did not pose a signficiant impact on APC outcomes (all p > 0.05). Conclusions: There was no correlation between rurality and outcomes in APC. The strategic and geographic allocation of cancer care delivery across 6 comprehensive treatment centers in British Columbia may serve as a model for other jurisdictions, particularly those that currently face outcome disparities in cancers that often require complex multidisciplinary care
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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 | 0.001 |
| Bibliometrics | 0.002 | 0.004 |
| Science and technology studies | 0.001 | 0.000 |
| Scholarly communication | 0.001 | 0.001 |
| Open science | 0.001 | 0.001 |
| Research integrity | 0.001 | 0.001 |
| Insufficient payload (model declined to judge) | 0.002 | 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".