Causes of urban-rural disparities in adjuvant chemotherapy (AC) for rectal cancer (RC).
Bibliographic record
Abstract
e14603 Background: While urban-rural differences in cancer care are well described, the etiology of these disparities is unclear. Our aims were to 1) characterize differences in AC use based on community size and 2) determine if such disparities are mediated through variations in driving distance (DD) and travel time (TT) to closest cancer center. Methods: Patients diagnosed with stage 2 and 3 RC from 1999 to 2009 and referred to any 1 of 5 regional cancer centers in British Columbia were reviewed. Communities were classified as rural, small urban, moderate urban and large urban based on census data. Using zip codes and a distance matrix application interface, DD and TT to the closest cancer center were determined and categorized into quartiles. Stepwise logistic regression models were constructed to explore AC use based on urban vs rural communities, adjusting for DD and TT. Results: A total of 3,017 patients were identified: median age was 67 years (IQR 58-75), 64% were men and 58% received AC. Patients were distributed across various communities: rural 36%; small urban 12%; moderate urban 13%; and large urban 39%. There were no differences in baseline patient and disease characteristics based on community size (all p>0.05). Compared to patients in large urban centers, those living in rural, small urban and moderate urban areas were less likely to be treated with AC (62 vs 49 vs 54 vs 58%, respectively, p<0.001). Likewise, DD and TT were shortest for large urban and longest for rural residents (both p<0.001). In multivariate analyses that controlled for confounders, urban-rural disparities in receipt of AC persisted, but these differences significantly diminished after adjusting for DD, TT, or both (Table). Conclusions: Urban-rural disparities in AC use is partly mediated by commute. Strategic distribution of cancer services that reduce DD and TT to cancer centers may improve access to AC for a number of RC patients who are living in smaller communities. [Table: see text]
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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 | 0.001 |
| 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) | 0.001 | 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 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".