Familial Risk Factors in Thyroid Cancer Across Generations and Geographics: A Systematic Review and Meta-Analysis
Bibliographic record
Abstract
The increasing global incidence of thyroid cancer highlights the importance of accurately assessing risk factors, particularly those related to family history. Although having affected family members is widely recognized as a risk factor for thyroid cancer, the exact degree of risk and its variation across types of familial relationships, parental gender, and geographic regions remain unclear. This systematic review and meta-analysis aimed to clarify the association between family history and thyroid cancer risk. We conducted a comprehensive literature search of PubMed, Web of Science, and Embase following PRISMA guidelines, identifying 13 studies from 503 initially screened. Statistical analyses were performed using random-effects models to estimate pooled odds ratios and risk ratios, with subgroup analyses to assess variations across population and relationship types. Our findings showed an approximately 4.5-fold higher risk of thyroid cancer in individuals with affected family members. Individuals with affected siblings were more likely to develop thyroid cancer while the risks associated with maternal and paternal family history were comparable in magnitude, with no statistical difference between them. Socioeconomic, educational, and lifestyle differences did not significantly influence risk, and geographic variations in familial risk could not be statistically confirmed by the subgroup analysis, in the context of high between-study heterogeneity. These results suggest that family history is a substantial risk factor for thyroid cancer, reinforcing the need for enhanced surveillance and screening strategies for those with a familial predisposition.
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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 | 0.009 | 0.026 |
| Meta-epidemiology (narrow) | 0.002 | 0.001 |
| Meta-epidemiology (broad) | 0.012 | 0.031 |
| Bibliometrics | 0.005 | 0.007 |
| Science and technology studies | 0.001 | 0.001 |
| Scholarly communication | 0.002 | 0.001 |
| Open science | 0.001 | 0.001 |
| Research integrity | 0.002 | 0.001 |
| Insufficient payload (model declined to judge) | 0.003 | 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".