Dietary pattern and risk of thyroid cancer: a systematic review and meta-analysis protocol
Bibliographic record
Abstract
Introduction Until now, the thyroid cancer case number has increased, and it is not entirely possible to attribute this continuous growth to more meticulous thyroid nodule selection and more accurate diagnostic techniques. While there is currently no conclusive evidence linking dietary factors to thyroid cancer, certain dietary patterns seem to have an impact on the development of the disease. There are interesting connections among diet, environment, metabolism and thyroid carcinogenesis; a deeper comprehension of the underlying mechanisms should help the identification of modifiable risk factors for thyroid cancer. This protocol aims to guide a systematic review and meta-analysis of the literature to search for an association between dietary pattern and risk of thyroid cancer. Methods and analysis The databases to search for observational studies will be PubMed, Embase, Scopus, Web of Science and LILACS, from inception to 10 December 2024. No language limitation or publication period will be imposed. The outcome will be the patients with thyroid cancer. Three impartial reviewers will choose the studies and extract data from the original publications. The Newcastle-Ottawa Quality Scale will assess the risk of bias, and the certainty of the evidence will be achieved by using the Grading of Recommendations Assessment, Development and Evaluation. The R (V.4.3.1) will be performed for data synthesis, and to measure heterogeneity, we will compute the I2 statistics. Additionally, a quantitative synthesis will be performed if the included studies are sufficiently homogenous. Ethics and dissemination It is not necessary to acquire ethical approval, as this study will be a review of the published data. A peer-reviewed publication will publish the systematic review’s findings. PROSPERO registration number International Prospective Register of Systematic Reviews (PROSPERO) CRD 42023463802.
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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 | 0.060 | 0.076 |
| Meta-epidemiology (narrow) | 0.006 | 0.005 |
| Meta-epidemiology (broad) | 0.019 | 0.018 |
| Bibliometrics | 0.010 | 0.009 |
| Science and technology studies | 0.003 | 0.004 |
| Scholarly communication | 0.006 | 0.005 |
| Open science | 0.005 | 0.004 |
| Research integrity | 0.007 | 0.005 |
| Insufficient payload (model declined to judge) | 0.081 | 0.008 |
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".