Lung cancer incidence in young women <i>vs</i>. young men: A systematic analysis in 40 countries
Bibliographic record
Abstract
Previous studies have reported converging lung cancer rates between sexes. We examine lung cancer incidence rates in young women vs. young men in 40 countries across five continents. Lung and bronchial cancer cases by 5-year age group (ages 30-64) and 5-year calendar period (1993-2012) were extracted from Cancer Incidence in Five Continents. Female-to-male incidence rate ratios (IRRs) and 95% confidence intervals (95%CIs) were calculated by age group and birth cohort. Among men, age-specific lung cancer incidence rates generally decreased in all countries, while in women the rates varied across countries with the trends in most countries stable or declining, albeit at a slower pace compared to those in men. As a result, the female-to-male IRRs increased among recent birth cohorts, with IRRs significantly greater than unity in Canada, Denmark, Germany, New Zealand, the Netherlands and the United States. For example, the IRRs in ages 45-49 year in the Netherlands increased from 0.7 (95% CI: 0.6-0.8) to 1.5 (95% CI: 1.4-1.7) in those born circa 1948 and 1963, respectively. Similar patterns, though nonsignificant, were found in 23 additional countries. These crossovers were largely driven by increasing adenocarcinoma incidence rates in women. For those countries with historical smoking data, smoking prevalence in women approached, but rarely exceeded, those of men. In conclusion, the emerging higher lung cancer incidence rates in young women compared to young men is widespread and not fully explained by sex differences in smoking patterns. Future studies are needed to identify reasons for the elevated incidence of lung cancer among young women.
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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 | 0.003 | 0.006 |
| Meta-epidemiology (narrow) | 0.001 | 0.001 |
| Meta-epidemiology (broad) | 0.002 | 0.007 |
| Bibliometrics | 0.007 | 0.011 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.001 | 0.001 |
| 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".