Does geography influence the treatment and outcomes of colorectal cancer? A population-based analysis
Bibliographic record
Abstract
BACKGROUND: The Canadian province of Manitoba covers a large geographical area but only has one major urban center, Winnipeg. We sought to determine if regional differences existed in the quality of colorectal cancer care in a publicly funded health care system. METHODS: This was a population-based historical cohort analysis of the treatment and outcomes of Manitobans diagnosed with colorectal cancer between 2004 and 2006. Administrative databases were utilized to assess quality of care using published quality indicators. RESULTS: A total of 2,086 patients were diagnosed with stage I to IV colorectal cancer and 42.2% lived outside of Winnipeg. Patients from North Manitoba had a lower odds of undergoing major surgery after controlling for other confounders (odds ratio (OR): 0.48, 95% confidence interval (CI): 0.26 to 0.90). No geographic differences existed in the quality measures of 30-day operative mortality, consultations with oncologists, surveillance colonoscopy, and 5-year survival. However, there was a trend towards lower survival in North Manitoba. CONCLUSION: We found minimal differences by geography. However, overall compliance with quality measures is low and there are concerning trends in North Manitoba. This study is one of the few to evaluate population-based benchmarks for colorectal cancer therapy in Canada.
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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 | 0.000 | 0.000 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.001 | 0.000 |
| Bibliometrics | 0.000 | 0.001 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.000 | 0.000 |
| Open science | 0.000 | 0.000 |
| Research integrity | 0.000 | 0.000 |
| 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".