Exposure to Per‐ and Polyfluoroalkyl Substances and the Risk of Prostate and Ovarian Cancer: An Epidemiologic Meta‐Analysis
Bibliographic record
Abstract
BACKGROUND: Per- and polyfluoroalkyl substances (PFAS) are persistent environmental contaminants. Previous research has linked PFAS exposure to prostate and ovarian cancer risk, however, the conclusions have been inconsistent. This research purpose was to determine the relationship between PFAS exposure and prostate and ovarian cancer at the population level. METHODS: We systematically reviewed three databases-PubMed, Web of Science, and Embase-for research from when these databases were established to April 15, 2024. The quality of the retrieved research was evaluated using the Newcastle-Ottawa Scale (NOS) quality measurement tool. Meta-analysis of the extracted data was conducted using Stata 18. We also conducted sensitivity and subgroup analyses, as well as Begg's and Egger's tests. RESULTS: Twelve publications were involved in the analysis for prostate cancer, and six were included for ovary cancer. The outcomes indicated that PFOS exposure was positively related to prostate cancer (OR: 1.13, 95% CI: 1.00-1.28), while mixed PFAS exposure was positively related to ovarian cancer (OR: 1.63, 95% CI: 1.49-1.78). The source of heterogeneity identified in the subgroup analysis was primarily attributable to variations in study design. No significant study bias was detected in the analysis. CONCLUSION: The study demonstrated an association between PFAS exposure and both prostate and ovarian cancers. Further investigation is required to clarify the underlying mechanisms and potential associations.
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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 | 0.016 | 0.028 |
| Meta-epidemiology (narrow) | 0.003 | 0.002 |
| Meta-epidemiology (broad) | 0.011 | 0.054 |
| Bibliometrics | 0.006 | 0.007 |
| Science and technology studies | 0.001 | 0.001 |
| Scholarly communication | 0.003 | 0.001 |
| Open science | 0.002 | 0.002 |
| Research integrity | 0.002 | 0.002 |
| Insufficient payload (model declined to judge) | 0.003 | 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".