Benzene exposure and risk of colorectal cancer by anatomical subsite in the Norwegian offshore petroleum workers cohort
Bibliographic record
Abstract
OBJECTIVE: To investigate the association between low levels of benzene exposure (≤0.879 parts per million [ppm]-years) and risk of colorectal cancer (CRC) including its anatomical subsites. METHODS: Among 25,347 male workers in the Norwegian Offshore Petroleum Workers (NOPW) cohort with offshore work history (1965-1998), 455 CRC cases were diagnosed 1999-2021. We compared these with a subcohort (n = 2031) drawn from the full cohort. Work histories were linked to a previously developed industry-specific benzene job-exposure matrix (JEM). Cox regression for case-cohort analyses was used to estimate hazard ratios (HRs) and 95% confidence intervals (CIs) for CRC, adjusted for age, body mass index, smoking, alcohol intake, red/processed meat intake, and physical activity. RESULTS: Risks of CRC increased with increasing benzene exposure. For all CRC, the HRs (95% CI) for the most exposed [quartile 4] vs. the unexposed were 1.32 (0.96 to 1.81, [0.177-0.879 ppm-years]; p-trend = 0.085) for cumulative, 1.52 (1.11 to 2.07, [17-34 years]; p-trend = 0.032) for duration, and 1.56 (1.15 to 2.12, [0.015-0.046 ppm]; p-trend = 0.005) for average intensity of benzene exposure. For right-sided colon cancer, the association was most evident for exposure duration (HR = 2.25 (1.33 to 3.80), quartile 4 [17-34 years] vs. unexposed; p-trend = 0.007). Sensitivity analyses showed consistent associations. CONCLUSION: This study found positive exposure-response associations between low-level benzene exposure and CRC risk in offshore petroleum workers. These findings add to emerging evidence that benzene can be associated with solid tumours including lung and bladder, which potentially has important occupational and public health implications.
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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 | 0.000 | 0.001 |
| 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".