A Comparative Analysis of Lung Cancer Incidence and Tobacco Consumption in Canada, Norway and Sweden: A Population-Based Study
Bibliographic record
Abstract
Risk factors for developing lung cancer include tobacco smoking, exposure to second-hand smoke, radon gas, asbestos and other carcinogens, as well as air pollution and aging. Canada, Norway and Sweden are three Arctic countries that are very similar in socioeconomic status, similar in the social determinants of health and many environmental factors, including current smoking rates. However, lung cancer incidence rates differ significantly among the three countries. Since tobacco smoking is the principal risk factor for developing lung cancer, and there is a long lag time between tobacco consumption and lung cancer incidence at the population average level, a comparative analysis is conducted with historical data available publicly in the three countries to demonstrate how tobacco consumption in the past impacts lung cancer incidence decades late. The analysis shows that lung cancer incidence rates increase proportionally with the increasing tobacco consumption rates with a lag time of 20-30 years. On average, one more cigarette smoked per person per day can increase lung cancer cases by 2 to 5 per 100,000 population.
Fetched live from OpenAlex and de-inverted. Abstracts are not stored in this database: the inverted indexes are 8.6 GB of the frame’s 9.3 GB of text, and the host has 13 GB free.
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 | 0.002 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.001 | 0.001 |
| Bibliometrics | 0.003 | 0.007 |
| Science and technology studies | 0.002 | 0.001 |
| Scholarly communication | 0.001 | 0.000 |
| Open science | 0.001 | 0.001 |
| Research integrity | 0.001 | 0.001 |
| Insufficient payload (model declined to judge) | 0.002 | 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".