Exposure to perfluoroalkyl and polyfluoroalkyl substances and risk of stroke in adults: a meta-analysis
Bibliographic record
Abstract
INTRODUCTION: Evidence of the adverse metabolic health effects of perfluoroalkyl and polyfluoroalkyl substances (PFAS) is increasing. However, the impact of PFAS on cardiovascular diseases remains controversial. This meta-analysis aimed to analyze the impact of PFAS on the stroke risk. CONTENT: Databases were searched for studies published up to November 1, 2022, which report the association between stroke and exposure to at least one of four main PFAS (perfluorooctanoic acid [PFOA], perfluorooctanesulfonic acid [PFOS], perfluorononanoic acid [PFNA], and perfluorohexane sulfonic acid [PFHxS]). Data extraction and quality assessment were performed according to the Newcastle-Ottawa scale. SUMMARY AND OUTLOOK: Four studies were included in this systematic review. Multivariate adjusted odds ratios (ORs) for incident stroke per 1-log unit increment in each serum PFAS were combined in the meta-analysis. The risk of development of stroke was not significantly associated with PFOA, PFOS, or PFNA exposure (PFOA: pooled odds ratio [OR]=1.001, 95 % confidence interval [CI]=0.975-1.028, p=0.934; PFOS: pooled OR=0.994, 95 % CI=0.972-1.017, p=0.601; PFNA: pooled OR=1.016, 95 % CI=0.920-1.123, p=0.752), whereas a moderately lower risk was associated with PFHxS exposure without statistical significance (pooled OR=0.953, 95 % CI=0.908-1.001, p=0.054). PFOA, PFOS, and PFNA exposure showed a neutral association, while PFHxS showed a possible inverse association with the risk of stroke. Therefore, this finding should be interpreted with caution. Further prospective observational studies with PFAS mixture analyses are warranted.
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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 | 0.023 |
| Meta-epidemiology (narrow) | 0.003 | 0.002 |
| Meta-epidemiology (broad) | 0.019 | 0.060 |
| Bibliometrics | 0.005 | 0.006 |
| Science and technology studies | 0.001 | 0.001 |
| Scholarly communication | 0.003 | 0.001 |
| Open science | 0.002 | 0.001 |
| 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".