Geographical Variation and Factors Associated with Non-Small Cell Lung Cancer in Manitoba
Bibliographic record
Abstract
BACKGROUND: Screening decreases non-small cell lung cancer (NSCLC) deaths and is recommended by the Canadian Task Force on Preventive Health Care. We investigated risk factor prevalence and NSCLC incidence at a small region level to inform resource allocation for lung cancer screening. METHODS: NSCLC diagnoses were obtained from the Canadian Cancer Registry, then geocoded to 283 small geographic areas (SGAs) in Manitoba. Sociodemographic characteristics of SGAs were obtained from the 2006 Canadian Census and Canadian Community Health Survey. Geographical variation was modelled using a Bayesian spatial Poisson model. RESULTS: NSCLC incidence in SGAs ranged from 1 to 343 cases per 100,000 population per year. The highest incidence rates were in the Southeastern, Southwestern, and Central regions of Manitoba, while most of Northern Manitoba had lower rates. Poisson regression suggested areas with higher proportions of Aboriginal people and higher average income, and immigrants had lower NSCLC incidence whereas areas with higher proportions of smokers had higher incidence. CONCLUSION: On an SGA level, smoking rates remain the most significant factor driving NSCLC incidence. Socioeconomic status and proportions of immigrants or Aboriginal peoples independently impact NSCLC rates. We have identified SGAs in Manitoba to target in policy and infrastructure planning for lung cancer screening.
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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) | 0.000 | 0.000 |
| Bibliometrics | 0.000 | 0.000 |
| Science and technology studies | 0.001 | 0.000 |
| Scholarly communication | 0.000 | 0.000 |
| Open science | 0.000 | 0.000 |
| Research integrity | 0.000 | 0.001 |
| 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".