Exposure to Polycyclic Aromatic Hydrocarbons and adverse reproductive outcomes in women: current status and future perspectives
Bibliographic record
Abstract
OBJECTIVES: Polycyclic Aromatic Hydrocarbons (PAHs) are ubiquitous, toxic environmental chemicals that can cause adverse reproductive health effects. The objectives of this mini-review are to highlight the adverse reproductive outcomes due to PAH exposure with the main focus on polycystic ovary syndrome (PCOS) and premature ovarian failure (POF) and to provide perspectives on future research needs. CONTENT: We reviewed studies that have reported the adverse reproductive outcomes associated with PAHs exposures in women through a comprehensive search of bibliographic databases and gray literature sources. In addition, potentially modifiable sources of exposure to PAHs and associated reproductive outcomes were also investigated. SUMMARY: A total of 232 papers were retrieved through a comprehensive search of bibliographic databases, out of which three studies met the eligibility criteria and were included in the review. Results showed that exposure to PAHs is associated with adverse reproductive outcomes defined as PCOS, POF, and reproductive hormone imbalance. Sources of PAH exposure associated with adverse reproductive outcomes include active and passive tobacco smoking, specific cooking methods, and pesticides. OUTLOOK: Future studies are warranted to examine the mechanisms by which PAHs result in adverse reproductive endpoints in women. Further, environmental exposures that are potentially modifiable such as exposure to tobacco smoke, may contribute to PAH exposure, and these exposures should be targeted in future policies and interventions.
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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 | 0.002 | 0.008 |
| Meta-epidemiology (narrow) | 0.001 | 0.000 |
| Meta-epidemiology (broad) | 0.002 | 0.003 |
| Bibliometrics | 0.004 | 0.005 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.002 | 0.002 |
| Open science | 0.001 | 0.001 |
| Research integrity | 0.001 | 0.001 |
| Insufficient payload (model declined to judge) | 0.005 | 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".