A structured review of the associations between breast cancer and exposures to selected organic solvents
Bibliographic record
Abstract
INTRODUCTION: Our objective was to identify published, peer-reviewed, epidemiological studies that estimated associations between the risk of developing or dying from malignant breast cancer and past exposure to selected organic solvents with reactive metabolites, to delineate the methods used and to synthesize the results. CONTENT: We undertook a structured review of case-control and cohort studies used to investigate breast cancer risk and exposure to selected organic solvents that produce reactive metabolites in the body. We used SCOPUS, MEDLINE (Ovid) and Web of Science databases from 1966 to December 31, 2023 to identify epidemiological studies that estimated associations between the risk of developing or dying from malignant breast cancer and past exposure to selected organic solvents with reactive metabolites and organic solvents combined as a group. SUMMARY: We described essential methodological characteristics of the 35 studies and presented quantitative results by individual solvent and other characteristics. We did not find compelling evidence that any of the selected organic solvents are implicated in the etiology of breast cancer. OUTLOOK: As millions of workers are exposed to organic solvents, this topic necessitates further investigation. Future research should focus on elucidating organic solvents that may contribute to the burden of breast cancer.
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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 | 0.003 | 0.011 |
| Meta-epidemiology (narrow) | 0.001 | 0.001 |
| Meta-epidemiology (broad) | 0.003 | 0.002 |
| Bibliometrics | 0.010 | 0.011 |
| Science and technology studies | 0.000 | 0.001 |
| Scholarly communication | 0.002 | 0.002 |
| Open science | 0.001 | 0.001 |
| Research integrity | 0.001 | 0.001 |
| Insufficient payload (model declined to judge) | 0.006 | 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".