Cancer survival outcomes in Ontario, Canada: Significant unexplained variation.
Bibliographic record
Abstract
36 Background: Cancer-specific outcomes are critical for assessing quality of care, and are key quality indicators for cancer control programs. Previous analyses of Ontario (Canada) data show that regional survival differences at the Local Health Integrated Network (LHIN) level exist for relative survival, overall survival, cancer-specific survival (CSS), and mortality rates.We sought to describe: 5-year cancer-specific survival (5Y-CSS) rates among Ontario LHINs; the impact of adjusting for known patient factors; and 5Y-CSS rates among patients diagnosed at Ontario's 50 largest cancer diagnosing hospitals. Methods: Newly diagnosed cases (colorectal, lung, breast, or prostate cancer) were identified in the Ontario Cancer Registry. Records were linked to data from CIHI and Statistics Canada, to identify date of diagnosis, cause-specific vital status, diagnosing hospital, and other reported variables. Cox regression models were used, and all models were adjusted for age and sex. Results: N = 498,382 incident cases (2007-2013) were included. 5Y-CSS varied across LHINs for all patients combined (range 62%-72%; p < 0.0001). Considering colorectal cancer cases as illustrative (N = 57,927), 5Y-CSS varied among LHINs from 58.4%-66.4% (p < 0.0001). Further adjusting for socioeconomic and urban-rural status minimally reduced that variation. Limiting the analysis cohort to patients diagnosed in one of Ontario's 50 largest hospitals (N = 43,245), 5Y-CSS ranged from 52% to 72% (p < 0.0001) among hospitals, and from 55% to 63% (p < 0.0001) among the hospitals affiliated with regional cancer centres. Comparable findings were seen for patients diagnosed with lung, breast, or prostate cancer. Collaborative staging data were available for a subset of patients; 5Y-CSS within all stage III patients (N = 5,360) ranged from 72% to 87%. Conclusions: Important, highly significant differences in cancer survival outcomes exist across Ontario. These are of great interest to patients, health-care providers, system administrators, and policy makers, and are not explained by adjusting for the variables included in these analyses.
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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 | 0.002 | 0.005 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.001 |
| Bibliometrics | 0.002 | 0.007 |
| Science and technology studies | 0.003 | 0.001 |
| Scholarly communication | 0.001 | 0.000 |
| Open science | 0.001 | 0.001 |
| Research integrity | 0.000 | 0.000 |
| 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".