The Nova Scotia Community Cancer Matrix: A geospatial tool to support cancer prevention
Bibliographic record
Abstract
Globally, cancer is a leading cause of death and morbidity and its burden is increasing worldwide. It is established that medical approaches alone will not solve this cancer crisis. Moreover, while cancer treatment can be effective, it is costly and access to treatment and health care is vastly inequitable. However, almost 50% of cancers are caused by potentially avoidable risk factors and are thus preventable. Cancer prevention represents the most cost-effective, feasible and sustainable pathway towards global cancer control. While much is known about cancer risk factors, prevention programs often lack consideration of how place impacts cancer risk over time. Maximizing cancer prevention investment requires an understanding of the geographic context for why some people develop cancer while others do not. Data on how community and individual level risk factors interact is therefore required. The Nova Scotia Community Cancer Matrix (NS-Matrix) study was established in Nova Scotia (NS), a small province in Eastern Canada with a population of 1 million. The study integrates small-area profiles of cancer incidence with cancer risk factors and socioeconomic conditions, to inform locally relevant and equitable cancer prevention strategies. The NS-Matrix Study includes over 99,000 incident cancers diagnosed in NS between 2001 and 2017, georeferenced to small-area communities. In this analysis we used Bayesian inference to identify communities with high and low risk for lung and bladder cancer: two highly preventable cancers with rates in NS exceeding the Canadian average, and for which key risk factors are high. We report significant spatial heterogeneity in lung and bladder cancer risk. The identification of spatial disparities relating to a community's socioeconomic profile and other spatially varying factors, such as environmental exposures, can inform prevention. Adopting Bayesian spatial analysis methods and utilizing high quality cancer registry data provides a model to support geographically-focused cancer prevention efforts, tailored to local community needs.
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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 | 0.009 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.001 |
| Bibliometrics | 0.008 | 0.009 |
| Science and technology studies | 0.001 | 0.000 |
| Scholarly communication | 0.002 | 0.001 |
| Open science | 0.001 | 0.002 |
| Research integrity | 0.000 | 0.000 |
| Insufficient payload (model declined to judge) | 0.014 | 0.002 |
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".