Per- and poly-fluoroalkyl substances (PFAS) Exposure and risk of bladder and prostate cancers: A systematic review and meta-analysis
Bibliographic record
Abstract
Objectives: PFASs are synthetic chemicals that humans may be exposed to through workplace or the environment. Previous studies have suggested a carcinogenic effect. In our review, we investigated the association between PFAS exposure and risk of bladder and prostate cancer. Methods: We searched through IARC Monographs, ATSDR documents, and PubMed (up to January 2024) to find studies that examined the relationship between PFAS exposure and bladder and prostate cancer. Four reviewers independently screened studies, extracted data, and evaluated quality using a modified version of the Newcastle-Ottawa Scale (NOS). We conducted meta-analyses using random-effects models, stratified analyses, dose-response assessments, and evaluated publication bias. Results: We included 21 independent studies in our meta-analysis. The findings didn’t reveal an association between PFOA, PFOS, and PFAS exposure and bladder cancer, as well PFOA, PFNA and prostate cancer. However, we found an association between prostate cancer and total PFAS (RR = 1.12, 95% CI =1.06-1.18), based on two studies, and an association of borderline statistical significance with PFOS (RR = 1.04, 95% CI =0.98-1.11). There was no difference between outcome, region, year of publication, study design, quality score, and gender, exposure source and different levels of PFASs for both cancer types. Publication bias was excluded for prostate cancer studies (P = 0.71) and bladder cancer (P = 0.79). Conclusion: Our research did not find a link between different types of PFAS exposure and bladder cancer. However, it supports a potential association between PFOS exposure and prostate cancer. Bias and confounding cannot be excluded.
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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 | 0.013 | 0.032 |
| Meta-epidemiology (narrow) | 0.003 | 0.002 |
| Meta-epidemiology (broad) | 0.020 | 0.044 |
| Bibliometrics | 0.008 | 0.010 |
| Science and technology studies | 0.001 | 0.001 |
| Scholarly communication | 0.004 | 0.002 |
| Open science | 0.003 | 0.002 |
| Research integrity | 0.003 | 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".