Social determinants of lung cancer incidence in Canada: A 13-year prospective study.
Bibliographic record
Abstract
BACKGROUND: The risk of lung cancer has been shown to be inversely related to socioeconomic status (SES). Because the Canadian Cancer Registry does not contain socioeconomic data, the 1991 Canadian Census Cohort was used to study social determinants of lung cancer risk in the general Canadian population. DATA AND METHODS: This study examines incidence rates of lung cancer and histologic subtypes by educational attainment, income and occupation in a broadly representative sample of Canadians aged 25 or older. Data for the 1991 Canadian Census Cohort were analyzed. The cohort comprised 2,734,835 individuals, among whom 215,700 new cancer cases were diagnosed from 1991 through 2003. Age-standardized incidence rates were calculated by age, sex, and SES using the direct method. Rate ratios, rate differences, and excess incidence were also calculated. RESULTS: An inverse risk between lung cancer incidence and educational attainment, income and occupation emerged among men and women, and a stepped negative gradient in RRs was evident for all SES variables and age groups. If all cohort members had experienced the rate of those with a university degree, lung cancer incidence would have been 56% lower in men and 55% lower in women. If all cohort members had experienced the incidence rate of those in the highest income quintile, incidence would have been 33% lower in men and 25% lower in women. If all cohort members had experienced the rate of those in managerial occupations, incidence would have been 54% lower in men and 44% lower in women. INTERPRETATION: A negative gradient in lung cancer risk was evident for all SES variables studied.
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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 | 0.001 |
| Meta-epidemiology (narrow) | 0.001 | 0.001 |
| Meta-epidemiology (broad) | 0.001 | 0.001 |
| Bibliometrics | 0.001 | 0.004 |
| Science and technology studies | 0.004 | 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) | 0.001 | 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".