PREVENTIVE APPROACHES TO HEALTH RISKS FROM ENDOCRINE-DISRUPTING CHEMICALS BISPHENOL-A (BPA): LITERATURE REVIEW
Bibliographic record
Abstract
Bisphenol-A (BPA) is a synthetic chemical widely used in the production of polycarbonate plastics and epoxy resins. BPA is classified as an endocrine-disrupting chemical (EDC) that can cause various adverse effects on human health. Biomonitoring studies in the United States, Germany, and Canada have shown that over 90% of the population have detectable levels of BPA in urine samples. This indicates widespread and sustained exposure in the general population. Furthermore, these findings underscore the importance of implementing preventive measures to mitigate the long-term health risks associated with BPA exposure. This article aims to review the literature on various preventive strategies to reduce health risks associated with BPA exposure. The method employed is a literature review, drawing references from Google Scholar, ScienceDirect, and PubMed databases, encompassing publications from 2020 to 2025. The study results indicate that preventive measures can be implemented through public education, strengthening regulations and policies, promoting lifestyle changes, and utilising antioxidants. Effective prevention requires cross-sectoral collaboration involving individual actions, strong public policies, continuous education, and an integrated scientific approach to create a safer and healthier environment free from BPA hazards.
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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 | 0.006 |
| Meta-epidemiology (narrow) | 0.001 | 0.000 |
| Meta-epidemiology (broad) | 0.002 | 0.002 |
| Bibliometrics | 0.011 | 0.010 |
| Science and technology studies | 0.001 | 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) | 0.005 | 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".