Contribution of smoking, disease history, and survival to lung cancer disparities in Black individuals
Bibliographic record
Abstract
BACKGROUND: Lung cancer is the leading cause of cancer deaths and disproportionately affects self-identified Black or African American ("Black") people, especially considering their relatively low self-reported smoking intensity rates. This study aimed to determine the relative impact of smoking history and lung cancer incidence risk, histology, stage, and survival on these disparities. METHODS: We used 2 lung cancer models (MichiganLung-All Races and MichiganLung-Black) to understand why Black people have higher rates of lung cancer deaths. We studied how different factors, such as smoking behaviors, cancer development, histology, stage at diagnosis, and lung cancer survival, contribute to these differences. RESULTS: Adjusted for smoking history, approximately 90% of the difference in lung cancer deaths between the overall and Black populations (born in 1960) was the result of differences in the risk of getting lung cancer. Differences in the histology and stage of lung cancer and survival had a small impact (4% to 6% for each). Similar results were observed for the 1950 and 1970 birth cohorts, regardless of their differences in smoking patterns from the 1960 cohort. CONCLUSIONS: After taking smoking into account, the higher rate of lung cancer deaths in Black people can mostly be explained by differences in the risk of developing lung cancer. As lung cancer treatments and detection improve, however, other factors may become more important in determining differences in lung cancer mortality between the Black and overall populations. To prevent current disparities from becoming worse, it is important to make sure that these improvements are available to everyone in an equitable way.
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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 | 0.002 | 0.004 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.001 |
| Bibliometrics | 0.001 | 0.000 |
| Science and technology studies | 0.001 | 0.000 |
| Scholarly communication | 0.001 | 0.000 |
| Open science | 0.000 | 0.001 |
| Research integrity | 0.000 | 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".