Data from Occupational-Related Exposure to Benzene and Risk of Cervical, Ovarian, and Endometrial Cancers: Systematic Review and Meta-analysis
Bibliographic record
Abstract
Abstract Benzene is a known cause of leukemia and other blood cancers, but its link to female genital cancers (ovarian, endometrial, and cervical) remains unclear. This meta-analysis evaluated the association between occupational benzene exposure and the risk of these cancers. A systematic search of PubMed, SCOPUS, and EMBASE identified 7,221 publications, with nine cohort studies meeting inclusion criteria. Summary risk ratios (RR) were calculated using Preferred Reporting Items for Systematic Reviews and Meta-Analyses; Meta-analysis of Observational Studies in Epidemiology; and Participants, Exposition, Comparators, Outcomes, and Study Design guidelines. Study quality was assessed with a modified Newcastle–Ottawa scale, and publication bias was evaluated via the Egger test and funnel plots. The overall summary RR for benzene exposure was 1.22 [95% confidence interval (CI), 1.03–1.44], primarily driven by mortality (RR = 1.69; 95% CI, 1.18–2.41) rather than incidence (RR = 1.08; 95% CI, 0.91–1.29). Cancer-specific RRs were 1.24 for cervical, 1.21 for endometrial, and 1.28 for ovarian cancers, none reaching statistical significance. No significant heterogeneity was found by cancer type, region, exposure duration, industry, or study quality. No publication bias was detected (P = 0.43). This analysis suggests a potential association between occupational benzene exposure and increased risk of female genital cancers, particularly in mortality data. However, the evidence remains inconclusive due to potential confounding factors and limitations in the available studies.
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 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 | 0.013 | 0.034 |
| Meta-epidemiology (narrow) | 0.003 | 0.002 |
| Meta-epidemiology (broad) | 0.017 | 0.041 |
| Bibliometrics | 0.008 | 0.009 |
| Science and technology studies | 0.001 | 0.001 |
| Scholarly communication | 0.003 | 0.002 |
| Open science | 0.002 | 0.002 |
| Research integrity | 0.002 | 0.002 |
| Insufficient payload (model declined to judge) | 0.004 | 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".