Promoter methylation of eight tumor-suppressor genes and the risk of thyroid cancer: A meta-analysis protocol v1
Bibliographic record
Abstract
Introduction The incidence of thyroid cancer is increasing and histological test by itself cannot differentiate thyroid cancer from some benign nodules. Our immediate goal is to meta-analysis and determines the impact of promoter methylation of eight selected candidate TSGs on thyroid cancer and to identify the most important molecules in this carcinogenesis pathway. Methods and analysis We will include observational studies evaluating the promoter methylation in patients with thyroid cancer. A comprehensive search was performed using PubMed, Scopus, and ISI Web of Knowledge databases, and eligible studies were included. The methodological quality of the included studies was evaluated according to the Newcastle Ottawa scale table and pooled odds ratios (ORs); 95% confidence intervals (CIs) were used to estimate the strength of the associations with Stata 12.0 software. Egger’s and Begg’s tests were applied to detect publication bias, in addition to the “metatrim” method. Ethics and dissemination No ethical issues are predicted. These findings will be published in a peer-reviewed journal and presented at national and international conferences. Registration number This systematic review protocol is registered in the PROSPERO International Prospective Register of Systematic Reviews, registration number (CRD42016033484). Strengths and limitations of this study This systematic review, for the first time, will conduct to evaluate the prognostic and diagnostic accuracy of DNA methylation in patients with thyroid cancer using comprehensive search of several databases. The study screening, data extraction, and risk of bias assessment of the current study will be conducted by two researchers independently. We expect some potential heterogeneities between previous studies, including stage, and histological grade in patient 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 | 0.039 | 0.081 |
| Meta-epidemiology (narrow) | 0.004 | 0.003 |
| Meta-epidemiology (broad) | 0.015 | 0.028 |
| Bibliometrics | 0.006 | 0.007 |
| Science and technology studies | 0.001 | 0.002 |
| Scholarly communication | 0.004 | 0.003 |
| Open science | 0.003 | 0.002 |
| Research integrity | 0.005 | 0.003 |
| Insufficient payload (model declined to judge) | 0.053 | 0.004 |
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".