Geographical Disparities of Rectal Cancer Local Recurrence and Outcomes
Bibliographic record
Abstract
BACKGROUND: Challenges exist in providing high-quality cancer treatments to populations spread over large geographical areas. Local recurrence of rectal cancer is a complicated clinical problem associated with high morbidity and mortality. OBJECTIVES: objectives of this study were to evaluate population-based rates and predictors of local recurrence of rectal cancer in the Province of Manitoba, Canada, with emphasis on the effects of geography. DESIGN: : This was a population-based retrospective analysis. Administrative data from the Manitoba Cancer Registry and individual patient charts were reviewed. SETTINGS: Patients with stages I to III rectal cancer who underwent surgery with curative intent in Manitoba between 2004 and 2006 were included. MAIN OUTCOME MEASURES: The primary outcome was the development of local recurrence after surgical resection. RESULTS: Three hundred seventy patients with a mean age of 67 years were identified. The 5-year local recurrence rate was 17.4%. In multivariate analysis, relative to Winnipeg residents, rural residents, regardless of where they underwent surgery, had an increased risk of local recurrence (HR, 3.47; 95% CI, 1.74-6.92 for surgery in Winnipeg; HR, 2.98; 95% CI, 1.59-5.57 for surgery in rural Manitoba). The absence of both neoadjuvant radiotherapy and adjuvant chemotherapy was associated with a higher risk of local recurrence. Higher risk of mortality was noted for rural patients (HR, 1.90; 95% CI, 1.24-2.89) and for those who developed local recurrence (HR, 2.01; 95% CI, 1.27-3.19). CONCLUSION: Local recurrence rates for rectal cancer are high in Manitoba. Geography is an important variable, because rural status is associated with higher local recurrence rates and decreased survival. The use of neoadjuvant radiotherapy was an important predictor of lower local recurrence rates. Further initiatives are imperative to identify why rural patients experience differences in outcomes in Manitoba.
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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 | 0.001 |
| Science and technology studies | 0.000 | 0.001 |
| Scholarly communication | 0.000 | 0.000 |
| Open science | 0.000 | 0.001 |
| Research integrity | 0.000 | 0.000 |
| Insufficient payload (model declined to judge) | 0.001 | 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 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".