BMI, Diet and Female Reproductive Factors as Risks for Thyroid Cancer: A Systematic Review
Bibliographic record
Abstract
BACKGROUND: Thyroid cancer incidence rates have been increasing worldwide but the reason behind this is unclear. Both the increasing use of diagnostic technologies allowing the detection of thyroid cancer and a true increase in thyroid cancer incidence have been proposed. This review assesses the role of body mass index (BMI), diet, and reproductive factors on the thyroid cancer trend. METHODS: Epidemiologic studies of the selected risk factors up to June 2010 were reviewed and critically assessed. RESULTS: Among the thirty-seven studies reviewed and despite variation in the risk estimates, most papers supported a small but positive association for BMI (risk estimate range: 1.1-2.3 in males and 1.0-7.4 in females.). Among specific dietary components, there was no consistent association of thyroid cancer risk with iodine intake through fortification (risk estimate range: 0.49-1.6) or fish consumption (risk estimate range 0.6-2.2), nor with diets high in cruciferous vegetables (risk estimate range 0.6-1.9). A small number of studies showed a consistent protective effect of diets high in non-cruciferous vegetable (risk estimate range: 0.71-0.92). Among reproductive factors (pregnancy, parity, number of live births, use of prescription hormones, menstrual cycle regularity, and menopausal status), none were consistently associated with higher thyroid cancer risk. CONCLUSIONS: BMI had the strongest link to thyroid cancer risk among those examined. Detailed examinations of population-level risk factors can help identify and support prevention efforts to reduce the burden of thyroid cancer.
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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 | 0.001 |
| Meta-epidemiology (narrow) | 0.001 | 0.000 |
| Meta-epidemiology (broad) | 0.006 | 0.001 |
| Bibliometrics | 0.000 | 0.000 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.000 | 0.000 |
| Open science | 0.000 | 0.000 |
| Research integrity | 0.000 | 0.000 |
| Insufficient payload (model declined to judge) | 0.000 | 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".