Vitamin and mineral supplements and thyroid cancer
Bibliographic record
Abstract
The purpose of this study was to consolidate epidemiological evidence for the association between dietary supplements of vitamins and minerals and thyroid cancer development, as well as to contribute to evidence-based dietary recommendations for thyroid cancer primary prevention. We carried out a systematic literature review specifically for dietary supplement and thyroid cancer risk. MEDLINE, EMBASE, and Dissertations and Theses were systematically searched to identify original epidemiological studies with a comparison group that investigated vitamin or mineral supplementation as an etiological factor for thyroid cancer. In total, 11 independent studies were identified and reviewed. Our qualitative summary showed conflicting results for common antioxidants including vitamins A, C, and E and β-carotene in relation to thyroid cancer. Similarly, results for dietary supplement combinations as well as other individual vitamins and minerals (vitamin B complex, vitamin D, iodine, calcium, zinc, magnesium, and iron) are largely inconsistent across studies. Overall, our review suggested that the current evidence to support any protective or hazardous effect of vitamin or mineral supplements on thyroid cancer development is inconclusive and additional studies addressing previous limitations are necessary to elucidate this possible association. In particular, reverse causality is of major concern and should be addressed by prospective studies with large and representative samples.
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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 | 0.005 |
| Meta-epidemiology (narrow) | 0.001 | 0.000 |
| Meta-epidemiology (broad) | 0.002 | 0.002 |
| Bibliometrics | 0.005 | 0.004 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.001 | 0.001 |
| Open science | 0.001 | 0.001 |
| Research integrity | 0.001 | 0.001 |
| Insufficient payload (model declined to judge) | 0.004 | 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".