Causes of urban-rural disparities in adjuvant chemotherapy (AC) for rectal cancer (RC).
Bibliographic record
Abstract
568 Background: While urban-rural differences in cancer care are well described, the etiology for such disparities is unclear. Our aim was to determine whether differences in AC use are mediated through variations in community size (CS) and whether differences in AC use translated into differences in patient survival outcomes (SO). Methods: Patients with stage II/III 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 (R), small urban (SU), moderate urban (MU), and large urban (LU) based on census data. Using zip codes and a distance matrix application interface, driving distance (DD) to patients’ respective cancer center (CC) was determined and categorized into quartiles. Stepwise logistic regression was used to explore AC use based on urban-rural communities, controlling for DD. Cox regression was used to identify whether variations in CS translated into differences in SO. Results: A total of 2618 patients were identified: median age 67 years (IQR 58-75), 64% men, and 63% received AC. Patients were distributed across various communities: R 37%; SU 11%; MU 13%; and LU 39%. There were no differences in baseline characteristics based on CS (all p<0.05). Compared to patients in LU centers, those living in R, SU and MU areas were less likely to be treated with AC (55 % vs. 49% vs. 71%, respectively, p<0.001). Likewise, DD was shortest for LU and longest for R residents (p<0.001). In multivariate analysis that controlled for confounders, urban-rural disparities in receipt of AC persisted, and these differences were significantly increased after adjusting for DD (Table). In Cox regression analysis which controlled for confounders, patients who lived in R, SU, and MU regions did not have a significantly inferior SO in comparison to those living in LU areas (HR: 0.926, 0.813, 1.006, respectively, p=0.47). Conclusions: There are urban-rural disparities in access to AC independent of patients’ DD to CC. These disparities do not seem to alter patients’ SO. [Table: see text]
Fetched live from OpenAlex and de-inverted. Abstracts are not stored in this database: the inverted indexes are 8.6 GB of the frame’s 9.3 GB of text, and the host has 13 GB free.
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 | 0.002 | 0.002 |
| 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) | 0.000 | 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".