Analysis of Lung Cancer Incidence in Non-Hispanic Black and White Americans using a Multistage Carcinogenesis Model
Bibliographic record
Abstract
PURPOSE: There are complex and paradoxical patterns in lung cancer incidence by race/ethnicity and gender; compared to non-Hispanic White (NHW) males, non-Hispanic Black (NHB) males smoke fewer cigarettes per day and less frequently but have higher lung cancer rates. Similarly, NHB females are less likely to smoke but have comparable lung cancer rates to NHW females. We use a multistage carcinogenesis model to study the impact of smoking on lung cancer incidence in NHB and NHW individuals in the Multiethnic Cohort Study (MEC). METHODS: The effects of smoking on the rates of lung tumor initiation, promotion, and malignant conversion, and the incidence of lung cancer in NHB versus NHW adults in the MEC were analyzed using the Two-Stage Clonal Expansion (TSCE) model. Maximum likelihood methods were used to estimate model parameters and assess differences by race/ethnicity, gender, and smoking history. RESULTS: Smoking increased promotion and malignant conversion but did not affect tumor initiation. Non-smoking-related initiation, promotion, and malignant conversion and smoking-related promotion and malignant conversion differed by race/ethnicity and gender. Non-smoking-related initiation and malignant conversion were higher in NHB than NHW individuals, whereas promotion was lower in NHB individuals. CONCLUSION: Findings suggest that while smoking plays an important role in lung cancer risk, background risk not dependent on smoking also plays a significant and under-recognized role in explaining race/ethnicity differences. Ultimately, the resulting TSCE model will inform race/ethnicity-specific lung cancer natural history models to assess the impact of preventive interventions on US lung cancer outcomes and disparities by race/ethnicity.
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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 | 0.004 | 0.005 |
| Meta-epidemiology (narrow) | 0.001 | 0.000 |
| Meta-epidemiology (broad) | 0.001 | 0.002 |
| Bibliometrics | 0.001 | 0.001 |
| Science and technology studies | 0.000 | 0.000 |
| 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".