Zinc Status and Occurrence of Thyroid Cancer: Systematic Review and Meta-Analysis
Bibliographic record
Abstract
Background and Objective: Thyroid cancer (TC) represents the most common group of endocrine tumors, and its incidence has increased over the last four decades. The imbalance of trace elements, such as zinc (Zn), has been investigated due to the thyroid’s sensitivity to these elements. Zn is essential for thyroid hormone action and may be involved in the pathogenesis of TC. This systematic review and meta-analysis aim to contribute to the discussion on the association between low serum Zn concentrations and the occurrence of TC. Materials and Methods: The search was carried out in the PubMed/MEDLINE, Scopus, Embase, LILACS and Web of Science databases, including observational studies published until December 2024. The primary outcome was low serum Zn concentration and the occurrence of TC. Three independent reviewers selected the studies and extracted the data from the original publications. The risk of bias was assessed using the Newcastle–Ottawa Quality Assessment Scale. Data analysis was performed using R software (V.4.3.1), and heterogeneity was calculated using the I2 statistic, with results based on random effects models. Results: A total of 10 studies (n = 7 case–control and n = 3 cross-sectional) with sample sizes ranging from 44 to 294 individuals were included. The results indicated that serum Zn levels were not significantly lower in patients with TC compared with healthy controls (mean difference: −251.77; 95% confidence interval: −699.09, 195.54; I2 = 100%, very low certainty). Conclusions: Further investigations, including rigorously designed observational studies with representative samples and improved control of potential confounding variables are indispensable.
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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 | 0.028 |
| Meta-epidemiology (narrow) | 0.002 | 0.001 |
| Meta-epidemiology (broad) | 0.015 | 0.027 |
| Bibliometrics | 0.006 | 0.008 |
| Science and technology studies | 0.001 | 0.001 |
| Scholarly communication | 0.003 | 0.001 |
| Open science | 0.002 | 0.001 |
| Research integrity | 0.002 | 0.001 |
| 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".