Assessment of five typical environmental endocrine disruptors and thyroid cancer risk: a meta-analysis
Bibliographic record
Abstract
Introduction: There are conflicting reports on the association between environmental endocrine disruptors (EEDs) and thyroid cancer. This meta-analysis aimed to elucidate the relationship between EEDs and thyroid cancer. Methods: We searched for epidemiological studies on EEDs and thyroid cancer published in PubMed and Web of Science up to December 2022. We then screened the articles that could extract data on EEDs concentration levels in both thyroid cancer patients and healthy controls. We excluded articles that could not calculate effect sizes, focused on other thyroid diseases, or lacked controls. Standardized mean difference (SMD) was calculated to analyze the association between EEDs and thyroid cancer. We measured the heterogeneity among the included studies using I2, assessed publication bias by Egger's and Begg's test, and evaluated article quality using the Newcastle-Ottawa Quality Score (NOS). In the end, fifteen eligible case-control studies were included. Results: Our comprehensive analysis revealed that polychlorinated biphenyls (PCBs) were negatively associated with thyroid cancer{ SMD = -0.03, 95% confidence interval (CI) = (-0.05, -0.00), P = 0.03}, while polybrominated diphenyl ethers (PBDEs), phthalates (PAEs), and heavy metals were positively associated with thyroid cancer{PBDEs: SMD = 0.14, 95%CI = (0.04, 0.23), P = 0.007; PAEs: SMD = 0.30, 95%CI = (0.02, 0.58), P = 0.04; heavy metals: SMD = 0.21, 95%CI = (0.11, 0.32), P < 0.001}. We did not find a statistically significant relationship between bisphenol A (BPA) and thyroid cancer. Most of the included studies did not show publication bias, except for those on PCBs. Discussion: Our results indicate that exposure to certain EEDs, such as PBDEs, PAEs, and heavy metals, increases the risk of thyroid cancer. However, further large-scale epidemiological studies and mechanism studies are needed to verify these potential relationships and understand the underlying biological mechanisms.
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How this classification was reachedexpand
Full frame distilled prediction
Teacher imitationNot calibrated prevalence, not ground truth. Human validation pending. Learned from the 10,348 direct Codex labels and 10,348 direct Gemma labels. Candidate is the union of thresholded teacher heads; consensus is their intersection. These outputs are machine_predicted_unvalidated and are not human labels or direct frontier model labels.
Codex and Gemma teacher scores by category
| Category | Codex | Gemma |
|---|---|---|
| Metaresearch | 0.000 | 0.000 |
| Meta-epidemiology (narrow) | 0.001 | 0.001 |
| Meta-epidemiology (broad) | 0.005 | 0.002 |
| Bibliometrics | 0.001 | 0.001 |
| Science and technology studies | 0.000 | 0.001 |
| Scholarly communication | 0.000 | 0.000 |
| Open science | 0.001 | 0.001 |
| Research integrity | 0.000 | 0.001 |
| Insufficient payload (model declined to judge) | 0.001 | 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 teacher head, 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".