Linking population-based survey and cancer registry data to examine the association between behaviours consistent with cancer prevention recommendations and cancer risk in Ontario
Bibliographic record
Abstract
IntroductionCertain subject behaviours and characteristics increase the risk of some cancer types (e.g., obesity, alcohol intake) while others reduce cancer risk (e.g., physical activity). In 2007, the World Cancer Research Fund (WCRF) and American Institute for Cancer Research (AICR) published recommendations to reduce cancer risk related to these behaviours.
 Objectives and ApproachThe objective is to examine the association between self-reported behaviour consistent with WCRF/AICR recommendations for body fatness, physical activity, vegetable/fruit consumption, and alcohol intake and the risk of all cancers combined and specific cancer types. The study cohort, comprised of the Canadian Community Health Survey (CCHS) Ontario sample, will be linked with health administrative databases, including the Ontario Cancer Registry to determine cancer outcomes. Individuals will be assessed for behaviours consistent with WCRF/AICR recommendations based on their responses to CCHS questions and the association of these behaviours with cancer risk will be explored using multivariable Cox proportional hazard regression models.
 ResultsTo detect a log hazard ratio of 1.10 (where a=0.05, power=0.80, proportion of the sample assigned to the exposure group=0.25 and R2=0.20), a sample size of 4,538 is required. Based on the number of records in the CCHS data frame (159,474) and an assumption that the CCHS sample experiences cancer incidence at a similar rate to the rest of the Ontario population, we expect to have 5,000 cancer cases for these analyses. Upon completion of the analysis, we will report hazard ratios that estimate the difference in cancer risk between individuals reporting behaviour consistent with the WCRF/AICR recommendations and those reporting behaviour not consistent with the recommendations.
 Conclusion/ImplicationsWCRF/AICR recommendations were developed as the basis for primary cancer prevention, both for individuals and population-wide policies and programs. The current study will quantify the difference in overall cancer risk between individuals who do and do not adhere to selected WCRF/AICR recommendations for the first time in a Canadian population.
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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 | 0.002 | 0.001 |
| 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 | 0.001 |
| 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".