Impact of Travel Distance and Urban‐Rural Status on the Multidisciplinary Management of Rectal Cancer
Bibliographic record
Abstract
OBJECTIVES: Optimal treatment of rectal cancer (RC) requires multidisciplinary care. We examined whether distance to treatment center or community size impacts access to multimodality care and population-based outcomes in RC. METHODS: Patients diagnosed with stage II/III RC from 1999 to 2009 and treated at 1 of 6 regional cancer centers in British Columbia were reviewed. Distance to treatment center was determined for each patient. Communities were classified as rural, small, medium, and large population centers. Logistic and Cox regression models assessed associations of distance and community size with treatment received as well as cancer-specific (CSS) and overall survival (OS). RESULTS: Of 3,158 patients, 93.6% underwent surgery, 86.3% received radiotherapy, and 51.3% were treated with adjuvant chemotherapy (AC). Median time from diagnosis to oncologic consultation was longer for those >100 km from a treatment center or residing in medium/rural communities. Logistic regression demonstrated no correlation between distance or community size and receipt of treatment modality. Univariate analysis showed similar CSS (P = .18, .88) and OS (P = .36, .47) based on community size and distance, respectively. In multivariate analysis, distance >100 km had inferior CSS (Hazard Ratio [HR] 1.39, 95% CI: 1.03-1.88; P = .031). There was no consistent trend between decreasing community size and outcomes; however, living in a small center was associated with improved OS (HR 0.58, 95% CI: 0.38-0.88; P = .011) and CSS (HR 0.42, 95% CI: 0.25-0.70; P = .001). CONCLUSIONS: In this population-based study, there were no urban-rural differences in access to multidisciplinary care, but increased distance may be associated with worse cancer-specific outcomes.
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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 | 0.000 | 0.003 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.000 | 0.001 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.000 | 0.000 |
| Open science | 0.000 | 0.001 |
| Research integrity | 0.000 | 0.000 |
| 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".