Cumulative cigarette tar exposure and lung cancer risk among Japanese smokers
Bibliographic record
Abstract
OBJECTIVE: Tar concentration in cigarette brands is chronologically decreasing in the USA and Japan. However, studies investigating lung cancer risk with cumulative tar exposure in Western and Asian countries are insufficient. To investigate the risk of lung cancer with cumulative cigarette tar exposure, we conducted a case-control study among Japanese current smokers. METHODS: This study used data from the US-Japan lung cancer joint study in 1993-1998. A total of 282 subjects with histologically confirmed lung cancer and 162 hospital and 227 community controls were included in the study, and two control groups were combined. The information regarding tar concentration was obtained from the published documents and additional estimation using the equation of regression. Cumulative tar concentration was calculated by multiplying the annual value of brand-specific tar concentration by years of smoking. The odds ratios and 95% confidence intervals for lung cancer with cumulative tar exposure were estimated using a logistic model. RESULTS: The odds ratios for lung cancer with both lower (1-59.8 × 105 mg) and higher (>59.8 × 105 mg) total cumulative tar exposure were statistically significant (3.81, 2.23-6.50 and 11.64, 6.56-20.67, respectively) with increasing trend (P < 0.001). The stratification analysis showed higher odds ratios in subjects with higher cumulative tar exposure regardless of inhalation, duration of smoking filtered cigarettes and histological type. CONCLUSIONS: This study showed that cumulative tar exposure is a dose-dependent indicator for lung cancer risk, and low-tar exposure was still associated with increased cancer risk.
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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 | 0.000 | 0.001 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.001 | 0.000 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.000 | 0.000 |
| Open science | 0.000 | 0.000 |
| 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".