Zinc and thyroid cancer: A systematic review and meta-analysis protocol
Bibliographic record
Abstract
INTRODUCTION: The thyroid cancer has the ninth larger incidence of cancer in the world. Investigations related to the exposure to metals have become important due to the sensibility of the thyroid gland to them. Studies reveal that carcinogenic progressions are associated to the deficiency of the essential trace elements. In this context, the zinc is highlighted, essential for the metabolism of the thyroidal hormone and has a potential relation with the pathogenesis of the thyroid cancer. The objective of this systematic review and meta-analysis is to evaluate the low serum zinc as a risk factor for thyroid cancer in adults. METHODS AND ANALYSIS: PubMed/MEDLINE, Scopus, Embase and LILACS databases will be searched for observational studies investigating the low serum zinc as a risk factor for thyroid cancer in adults. No language or publication period restrictions will be imposed. The primary outcome will be that the low serum zinc is a risk factor for thyroid cancer. Three independent reviewers will select the studies and extract data from the original publications. The risk-of-bias will be assessed by using the Newcastle-Ottawa Quality Assessment Scale (NOS). Data synthesis will be performed using the R software (V.4.3.1) and to assess heterogeneity, we will compute the I2 statistic and the results will be based on either random-effects or fixed-effects models, depending on the heterogeneity. The Grading of Recommendations, Development, and Evaluation (GRADE) system will be used to evaluate the reliability and quality of evidence. PROSPERO REGISTRATION NUMBER: International Prospective Register of Systematic Reviews (PROSPERO) CRD42023463747.
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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 | 0.042 | 0.063 |
| Meta-epidemiology (narrow) | 0.005 | 0.004 |
| Meta-epidemiology (broad) | 0.024 | 0.020 |
| Bibliometrics | 0.011 | 0.009 |
| Science and technology studies | 0.003 | 0.003 |
| Scholarly communication | 0.006 | 0.005 |
| Open science | 0.005 | 0.004 |
| Research integrity | 0.006 | 0.004 |
| Insufficient payload (model declined to judge) | 0.064 | 0.005 |
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".