The effects of livestock artificial hormones on the human reproductive and endocrine systems – a meta-analysis
Bibliographic record
Abstract
The growing global demand for meat and dairy has led many livestock producers to use artificial hormones to speed up animal growth and increase production. While this boosts efficiency, it raises serious concerns for human health. Hormones like melengestrol, zeranol, progesterone, testosterone and estradiol can disrupt the human endocrine system, potentially causing fertility issues, hormonal imbalances and long-term health problems. This study reviews the health effects of consuming hormone-treated animal products through a meta-analysis of peer-reviewed articles published after 2012. Sources were selected from databases such as PubMed, Google Scholar and ResearchGate, focusing on studies with strong methods and direct relevance to human health. The results reveal health risks linked to hormone residues and highlight the gaps in public awareness and regulation. A comparison of global policies shows clear differences: the European Union bans several hormones, Canada enforces strict monitoring, while the U.S. allows a limited use under regulation. These policy gaps show the need for unified global standards. The study calls for stronger regulation, better consumer education, and safer, hormone-free farming practices to protect public health and ensure a more sustainable food system.
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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 | 0.012 | 0.024 |
| Meta-epidemiology (narrow) | 0.002 | 0.001 |
| Meta-epidemiology (broad) | 0.007 | 0.037 |
| Bibliometrics | 0.004 | 0.005 |
| 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) | 0.005 | 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".