Summary and Concluding Remarks: Patterns of Birth Cohort‒Specific Smoking Histories
Bibliographic record
Abstract
The Cancer Intervention and Surveillance Modeling Network (CISNET) Lung Working Group age-period-cohort methodology to study smoking patterns can be applied to tackle important issues in tobacco control and public health. This paper summarizes the analyses of smoking patterns in the U.S. by race/ethnicity, educational attainment, and family income and for each of the 50 U.S. states using the CISNET Lung Working Group age-period-cohort approach. We describe how decision makers, policy advocates, and researchers can use the sociodemographic analyses in this supplement to project state smoking trends and develop effective state-level tobacco control strategies. The all-cause mortality RR estimates associated with smoking for U.S. race/ethnicity and education groups are also discussed in the context of research that measures and evaluates health disparities. Finally, the application of the CISNET Lung Working Group age-period-cohort methodology to Brazil is reviewed with a view to how the same types of analyses can be applied to other low- and middle-income countries.
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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 | 0.020 | 0.060 |
| Meta-epidemiology (narrow) | 0.003 | 0.001 |
| Meta-epidemiology (broad) | 0.003 | 0.003 |
| Bibliometrics | 0.003 | 0.002 |
| Science and technology studies | 0.002 | 0.003 |
| Scholarly communication | 0.006 | 0.005 |
| Open science | 0.005 | 0.001 |
| Research integrity | 0.009 | 0.019 |
| Insufficient payload (model declined to judge) | 0.006 | 0.005 |
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".