Does community size impact survival with breast cancer? Data from a large population-based cohort in British Columbia.
Bibliographic record
Abstract
e18618 Background: Patients with breast cancer residing in rural settings have a unique set of challenges in accessing cancer care. How rural residence impacts disease-related outcomes is not fully understood. This study is a descriptive and quantitative analysis of geographically determined differences in patient presentations, treatment choices and outcomes for individuals diagnosed with breast cancer in rural and urban communities in British Columbia. Methods: Using BC Cancer’s Breast Cancer Outcomes Unit database, we identified all patients referred with newly diagnosed invasive breast cancer at any stage between 2005-2018. Using postal code, we then categorized patients as residing in either an urban (population ≥ 100,000) or rural setting ( < 100,000), using the Statistics Canada classification of community size. We analyzed baseline clinical-pathological features, patterns of initial treatment, and outcomes differences between urban and rural settings. We performed a univariable analysis examining differences in locoregional relapse, distant relapse and breast cancer-specific survival (BCSS) between urban and rural cohorts. We then performed a multivariable analysis accounting for age, grade, lymphovascular invasion (LVI), subtype, stage, initial treatment with chemotherapy, endocrine therapy (ET), radiotherapy (RT) and type of definitive surgery. Results: The median follow up was 9.7 years for 35,255 patients. 9244 patients in rural and 26011 in an urban setting. There were no clinically meaningful differences in age, LVI, grade, subtype and stage at diagnosis between the urban and rural cohorts. Patients residing in a rural setting were significantly more likely to be treated with mastectomy (43.4% vs. 39.1%), less likely to receive RT (61.4% vs. 67.7%) and less likely to receive ET (67.2% vs. 71.7%). However, there was no difference in use of chemotherapy or anti-HER2 therapies between cohorts. On univariable analysis, urban residency was associated with improved BCSS (86.5% [86.0-86.9%] vs. 85.3% [95% CI: 84.5-86.1%], p < 0.001), and lower risk of distant relapse (12.1% [11.7-12.6%] vs. 13.4% [12.6-14.1%], p < 0.001). There was no influence on locoregional relapse. On multivariable analysis, urban residency was associated with improved BCSS, with a hazard ratio (HR) of 0.92 [0.86-0.99, p = 0.03], and lower risk of distant relapse (HR = 0.91 [0.85-0.98, p = 0.01]), but no influence on locoregional relapse. Conclusions: Rural residency is a significant and independent risk factor for both distant relapse and mortality from breast cancer. It is also associated with less use of ET and RT. Understanding the factors that contribute to these disparities is necessary to close the gap between rural and urban breast cancer 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 | 0.001 | 0.003 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.001 | 0.000 |
| Bibliometrics | 0.002 | 0.005 |
| Science and technology studies | 0.002 | 0.000 |
| Scholarly communication | 0.001 | 0.000 |
| Open science | 0.001 | 0.001 |
| Research integrity | 0.001 | 0.001 |
| Insufficient payload (model declined to judge) | 0.003 | 0.001 |
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".