Critical Literature Review of Determinants and Levels of Occupational Benzene Exposure for United States Community-Based Case-Control Studies
Bibliographic record
Abstract
This article presents the results of an extensive literature review identifying the uses or occurrences of, and exposures to, benzene in a variety of industries for a community-based case-control study of childhood brain cancer in the United States and Canada. We focused on industries for which quantitative exposure data were identified in studies conducted in North America in the 1980s. Each industry was coded according to the 1987 Standard Industrial Classification (SIC) system. For each industry, information relevant to exposure assessment, including process descriptions, job titles, tasks, and work practices, was summarized when available. Estimates of probability and intensity of exposure, and our confidence in these estimates are presented. Arithmetic means (AMs), weighted for the number of measurements for each industry, were calculated based on measurement data from long-term (i.e., 60+ minutes) personal sampling; short-term or area samples were only used when no other data were available for a given industry. Industries for which no quantitative exposure levels were identified in the North American literature but for which information was found on benzene use are briefly described. Published exposure data indicate that workers in most industries in the 1980s experienced exposure levels below the current standard of 1 part per million (ppm), with a weighted AM of 0.33 ppm across all industries. Despite the longtime recognition of the hematological effects of benzene, little information was available on exposure levels and determinants for many industries with potential exposure. Nevertheless, this review may clarify some of the procedures involved in assessing occupational exposures in community-based studies and may aid in the interpretation of previous occupational studies that relied on job title or industry.
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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 | 0.020 | 0.076 |
| Meta-epidemiology (narrow) | 0.001 | 0.001 |
| Meta-epidemiology (broad) | 0.004 | 0.005 |
| Bibliometrics | 0.032 | 0.026 |
| Science and technology studies | 0.001 | 0.001 |
| Scholarly communication | 0.003 | 0.003 |
| Open science | 0.002 | 0.002 |
| Research integrity | 0.002 | 0.002 |
| Insufficient payload (model declined to judge) | 0.004 | 0.001 |
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".