Pooled study of occupational exposure to aromatic hydrocarbon solvents and risk of multiple myeloma
Bibliographic record
Abstract
OBJECTIVES: To investigate the association between occupational exposure to aromatic hydrocarbon solvents and risk of multiple myeloma (MM) in a large, consortium-based study. METHODS: We pooled data on 2854 cases and 10 743 controls from nine studies participating in the InterLymph consortium. Occupational exposures to benzene, toluene and xylene were assigned by a job-exposure matrix, coupled with 'correction' of exposure probability by self-reported or expert-assessed exposure from the individual studies. Cumulative intensity was calculated as the job-specific exposure intensity multiplied by job duration, summed across jobs. Associations were estimated using logistic regression, with inclusion of covariates for study matching factors and other potential confounders. We repeated our main analysis using random-effects meta-analysis to evaluate heterogeneity of effect. RESULTS: Benzene, toluene and xylene were each associated with MM. For the three solvents, the highest quartile of high-probability cumulative intensity exposure (vs unexposed) was associated with 42% to 63% increased risks of MM. Associations with toluene and xylene exposures were fairly consistent and robust to sensitivity analyses. The estimated effect for benzene was moderately heterogeneous between the studies. Each solvent's association with MM was stronger for exposure occurring within 20 years of diagnosis than with exposure lagged by more than 20 years. CONCLUSIONS: Our study adds important evidence for a role of aromatic hydrocarbon solvents in causation of MM. The difficulty in disentangling individual compounds in this group and a lack of data on potential carcinogenicity of toluene and xylene, in widespread current use, underscore a need for further epidemiological evaluation.
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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 | 0.000 | 0.000 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.001 | 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) | 0.000 | 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".