Socioeconomic status and lung cancer risk in Canada
Bibliographic record
Abstract
BACKGROUND: Several epidemiological studies have found that lung cancer is inversely related to socioeconomic status (SES) and suggest it as a possible risk factor for lung cancer. This study examines SES and lung cancer risk in Canada. METHODS: Mailed questionnaires with telephone follow-up were used to obtain data on 3280 newly diagnosed, histologically confirmed lung cancer cases and 5073 population controls, between 1994 and 1997, in eight Canadian provinces. Measurement included information on SES, smoking habits, alcohol use, diet, residential and occupational histories and both residential and occupational exposure to environmental tobacco smoke (ETS). Odds ratios (OR) and 95% CI were derived from unconditional logistic regression analysis. RESULTS: Compared with high income adequacy, an increased risk was found among low income males and females, with adjusted OR of 1.7 (95% CI : 1.3-2.2) and 1.5 (95% CI : 1.1-2.0), respectively. Compared with < or = 8 years of education, the adjusted OR were 0.6 (95% CI : 0.5-0.7) and 0.6 (95% CI : 0.5-0.8) for > or = 14 years education among males and females, respectively. Lung cancer risk was significantly increased for males of some social classes. The population attributable risk for income adequacy, education and social class was 24%, 25% and 21% among males, respectively, and 14% and 19% for income adequacy and education among females, respectively, in this Canadian population. CONCLUSIONS: A statistically significant association between income adequacy, education social class and lung cancer risk was found.
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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 | 0.000 |
| Bibliometrics | 0.001 | 0.002 |
| Science and technology studies | 0.002 | 0.000 |
| Scholarly communication | 0.001 | 0.000 |
| Open science | 0.000 | 0.000 |
| Research integrity | 0.000 | 0.000 |
| 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".