Assessment of the Ecological Association between Tobacco Smoking Exposure and Bladder Cancer Incidence over the Past Half-Century in the United States
Bibliographic record
Abstract
Background: Since tobacco smoking represents the most established risk factor for bladder cancer, we sought to assess the ecological association between tobacco smoking prevalence and bladder cancer incidence and to contrast it with lung cancer. Methods: The annual overall tobacco smoking prevalence rates were extracted from the Report of the Surgeon General and the Center for Disease Control between 1953 and 1983. The overall age-adjusted incidence rates for bladder and lung cancers were derived from the Surveillance, Epidemiology, and End Results database between 1983 and 2013 (30-year latency period). Weighted least square regression models were used to assess bladder and lung cancer incidence rate differences (IRD) related to trends in tobacco smoking prevalence. A Wald test was used to compare whether the prevalence of tobacco smoking, as an explanatory variable, differentially predicts bladder versus lung cancer incidence rates. Results: The associations between tobacco smoking prevalence and bladder cancer incidence were not significant in the overall (IRD = +0.04; 95%CI (−0.14; +0.22); p = 0.63), male (IRD = +0.07; 95%CI (−0.09; +0.23); p = 0.37), or female (IRD = +0.12; 95%CI (−0.01; +0.25); p = 0.06) populations. There was an association between tobacco smoking prevalence and lung cancer incidence in the overall (IRD: +3.55; 95%CI ( +3.09; +4.00); p < 0.001), male (IRD: +4.82; 95%CI (+4.44; +5.20); p < 0.001), and female (IRD: +3.55; 95%CI (+3.12; +3.99); p < 0.001) populations. The difference between the observed associations of tobacco smoking prevalence with bladder versus lung cancer incidence was also significant in all examined populations (p < 0.001). Conclusions: Variations in tobacco smoking prevalence only partially explained the trends in the incidence of bladder cancer, indicating that its etiology is complex.
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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) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.001 |
| Bibliometrics | 0.002 | 0.002 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.001 | 0.000 |
| Open science | 0.000 | 0.001 |
| Research integrity | 0.000 | 0.000 |
| 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".