O-59 Increased lung cancer risk and occupational benzene exposure: results from a pooled case-control study
Bibliographic record
Abstract
<h3>Introduction</h3> Benzene is widely present in various industries and ubiquitously in the general environment. Benzene has been classified as a known human carcinogen, but there is limited evidence linking benzene exposure with lung cancer. However, if such an association exists, this could have large implications for occupational and environmental risk assessment. We aimed to systematically investigate the association between occupational benzene exposure and lung cancer. <h3>Material and Methods</h3> Subjects from 14 case-control studies across Europe and Canada were pooled. We used a quantitative job-exposure matrix (BEN-JEM) to estimate benzene exposure based on occupation records. Logistic regression models were used to estimate lung cancer risk and various benzene exposure indices. We stratified analyses by smoking status and lung cancer subtypes, and rigorously adjusted for age, sex, smoking and other known occupational lung carcinogens. <h3>Results and Conclusion</h3> Analyses included 28048 subjects (12329 cases, 15719 controls). Lung cancer odds ratios ranged from 1.12 (95% CI 1.03-1.22) to 1.32 (95% CI 1.18-1.48) for groups with the lowest and highest cumulative exposure, respectively. An increasing trend was observed with duration of exposure (P<0.001), while lung cancer risk decreased with increasing time since last exposure (P=0.02). These effects were seen for all lung cancer subtypes, in current, former and never smokers, and for both sexes, and were not unduly influenced by any particular occupational group or study. Based on our study in the general population, we found strong, consistent, and robust evidence linking occupational benzene exposure with lung cancer. By rigorously adjusting for smoking and other occupational exposures, our findings provide strong support for the association between benzene exposure and lung cancer. Such a link has a large implication for occupational and environmental risk assessment and reinforces the need to further reduce benzene exposure globally.
Fetched live from OpenAlex and de-inverted. Abstracts are not stored in this database: the inverted indexes are 8.6 GB of the frame’s 9.3 GB of text, and the host has 13 GB free.
How this classification was reachedexpand
Full frame distilled prediction
Teacher imitationNot calibrated prevalence, not ground truth. Human validation pending. Learned from the 10,348 direct Codex labels and 10,348 direct Gemma labels. Candidate is the union of thresholded teacher heads; consensus is their intersection. These outputs are machine_predicted_unvalidated and are not human labels or direct frontier model labels.
Codex and Gemma teacher scores by category
| Category | Codex | Gemma |
|---|---|---|
| Metaresearch | 0.001 | 0.001 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.000 | 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 teacher head, 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".