Cancer incidence attributable to tobacco in Alberta, Canada, in 2012
Bibliographic record
Abstract
BACKGROUND: Strong and consistent epidemiologic evidence shows that tobacco smoking causes cancers at various sites. The purpose of this study was to quantify the proportion and total number of site-specific cancers in Alberta attributable to tobacco exposure. METHODS: The proportion of incident cancer cases attributable to active and passive tobacco exposure in Alberta was estimated with population attributable risks. Data from the Canadian Community Health Survey (CCHS) for 2000-2007 were used to estimate prevalence of active (current or former smoker) and passive (second-hand smoke) tobacco exposure in Alberta. RESULTS: According to the 2000/01 CCHS, 29.1% and 38.6% of Albertans were estimated to be current and former smokers, respectively. According to the 2003 CCHS, 23.7% of Albertans who had never smoked reported regular second-hand exposure to tobacco. Population attributable risk estimates for tobacco-related cancer sites ranged from about 4% for ovarian cancer to 74% for laryngeal cancer. About 5% of incident lung cancers in men and women who never smoked could be attributed to passive tobacco exposure. Overall, 37.0% of tobacco-related cancers in Alberta (or 15.7% of all cancers) were estimated to be attributable to active tobacco smoking in 2012. INTERPRETATION: A notable proportion of cancers associated with tobacco use were estimated to be attributable to active smoking in Alberta. Strategies to reduce the prevalence of active tobacco smoking in Alberta could have a considerable impact on future cancer incidence.
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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 | 0.000 | 0.001 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.001 |
| Bibliometrics | 0.002 | 0.005 |
| Science and technology studies | 0.001 | 0.000 |
| Scholarly communication | 0.001 | 0.000 |
| Open science | 0.001 | 0.001 |
| Research integrity | 0.000 | 0.001 |
| Insufficient payload (model declined to judge) | 0.003 | 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".