Will metformin use lead to a decreased risk of thyroid cancer? A systematic review and meta-analyses
Bibliographic record
Abstract
BACKGROUND: It has been reported that metformin use may reduce the risk of thyroid cancer, but existing studies have generated inconsistent results. The purpose of this study was to investigate such association between metformin use and the risk of thyroid cancer. METHODS: Studies of metformin use for the risk of thyroid cancer were searched in Web of Science, PubMed, Embase, Cochrane Library, China National Knowledge Infrastructure, China Biomedical Database, Wanfang Data, and Chinese Scientific Journals Database (VIP) from the establishment date to December 2022. Newcastle-Ottawa scale is adopted for assessing the methodological quality of included studies, and the inter-study heterogeneity was assessed by using the I-squared statistic. Combined odds ratios (ORs) with the corresponding 95% confidence intervals (CIs) were calculated through either fixed-effects or random-effects model according to the heterogeneity. Besides, subgroup analyses, sensitivity analyses and test for publication bias were conducted. RESULTS: Five studies involving 1,713,528 participants were enrolled in the qualitative and quantitative synthesis. The result of the meta-analyses showed that metformin use was associated with a statistically significant lower risk of thyroid cancer (pooled OR = 0.68, 95% CI = 0.50-0.91, P = 0.011). Moreover, in the subgroup analysis, we found that the use of metformin may also aid in the prevention of thyroid cancer in Eastern population (pooled OR = 0.55, 95% CI = 0.35-0.88, P = 0.012) rather than Western population (pooled OR = 0.89, 95% CI = 0.52-1.54, P = 0.685). Sensitivity analysis suggested the results of this meta-analyses were relatively stable. No publication bias was detected. CONCLUSION: Metformin use is beneficial for reducing the risk of thyroid cancer. For further investigation, more well-designed studies are still needed to elucidate the association between metformin use and the risk of thyroid cancer.
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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 | 0.016 | 0.038 |
| Meta-epidemiology (narrow) | 0.003 | 0.002 |
| Meta-epidemiology (broad) | 0.017 | 0.043 |
| Bibliometrics | 0.007 | 0.007 |
| Science and technology studies | 0.001 | 0.001 |
| Scholarly communication | 0.003 | 0.002 |
| Open science | 0.003 | 0.002 |
| Research integrity | 0.003 | 0.002 |
| Insufficient payload (model declined to judge) | 0.003 | 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".