Effect of Place of Residence and Treatment on Survival Outcomes in Patients With Diffuse Large B-Cell Lymphoma in British Columbia
Bibliographic record
Abstract
BACKGROUND: We examined the relationship between location of residence at the time of diagnosis of diffuse large B-cell lymphoma (DLBCL) and health outcomes in a geographically large Canadian province with publicly funded, universally available medical care. PATIENTS AND METHODS: The British Columbia Cancer Registry was used to identify all patients 18-80 years of age diagnosed with DLBCL between January 2003 and December 2008. Home and treatment center postal codes were used to determine urban versus rural status and driving distance to access treatment. RESULTS: We identified 1,357 patients. The median age was 64 years (range: 18-80 years), 59% were male, 50% were stage III/IV, 84% received chemotherapy with curative intent, and 32% received radiotherapy. There were 186 (14%) who resided in rural areas, 141 (10%) in small urban areas, 183 (14%) in medium urban areas, and 847 (62%) in large urban areas. Patient and treatment characteristics were similar regardless of location. Five-year overall survival (OS) was 62% for patients in rural areas, 44% in small urban areas, 53% in medium urban areas, and 60% in large urban areas (p = .018). In multivariate analysis, there was no difference in OS between rural and large urban area patients (hazard ratio [HR]: 1.0; 95% confidence interval [CI]: 0.7-1.4), although patients in small urban areas (HR: 1.4; 95% CI: 1.0-2.0) and medium urban areas (HR: 1.4; 95% CI: 1.0-1.9) had worse OS than those in large urban areas. CONCLUSION: Place of residence at diagnosis is associated with survival of patients with DLBCL in British Columbia, Canada. Rural patients have similar survival to those in large urban areas, whereas patients living in small and medium urban areas experience worse 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 | 0.002 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.001 | 0.002 |
| Science and technology studies | 0.001 | 0.001 |
| Scholarly communication | 0.001 | 0.000 |
| Open science | 0.001 | 0.001 |
| Research integrity | 0.000 | 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".