Characterizing Regional Variability in Lung Cancer Outcomes across Ontario—A Population-Based Analysis
Bibliographic record
Abstract
Background: Lung cancer (LC) is the leading cause of cancer-related mortality. In Ontario, Canada, there are significant survival differences for patients with newly diagnosed LC across the 14 provincial regions. Methods: A population-based retrospective cohort study using ICES databases from 01/2007–12/2017 identified patients with newly diagnosed LC through the Ontario Cancer Registry and those with LC as the cause of death. Descriptive data included patient, disease, and system characteristics. The primary outcome was 5-year survival by region. Results: 178,202 patient records were identified; 101,263 met inclusion criteria. LC incidence varied by region (5.6–14.6/10,000), as did histologic subtype (adenocarcinoma: 27.3–46.1%). Five-year cancer-specific survival was impacted by age, rurality, pathologic subtype, stage at diagnosis, and income quintile. Timely care was inversely related to survival (fastest quintile: HR 3.22, p < 0.0001). Adjusted 5-year cancer-specific survival varied across regions (24.1%, HR 1.12; 34.0%, HR 0.89, p < 0.001). Conclusions: When adjusting for confounders, differences in survival by health region persisted, suggesting a complex interplay between patient, disease, and system factors. A single approach to improving patient care is likely to be ineffective across different systems. Quality improvement initiatives to improve patient outcomes require different approaches amongst health regions to address local disparities in care.
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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) | 0.000 | 0.001 |
| Bibliometrics | 0.002 | 0.005 |
| Science and technology studies | 0.002 | 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) | 0.001 | 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".