Association between Type 2 Diabetes Mellitus and Risk of Development of Oral Cancer: A Systematic Review and Meta-analysis
Bibliographic record
Abstract
ABSTRACT Aim: To assess the risk of developing oral cancer (OC) in individuals who have diabetes mellitus (DM) and to predict the prognosis for OC patients with DM. Methodology: The review adhered to Preferred Reporting Items for Systematic Reviews and Meta-analysis (PRISMA) 2020 guidelines and was registered in PROSPERO (CRD42024517197). A thorough search of databases was conducted from January 2000 to May 2024 to identify studies reporting the reporting association between DM and the development of OC. Quality assessment was performed using the Newcastle Ottawa Scale for included studies. The odds ratio (OR) and risk ratio (RR) served as the summary statistic measure, employing a random-effect model with a significance threshold set at P < 0.05. Results: Twelve studies met the eligibility criteria and underwent qualitative synthesis, with eight studies in meta-analysis. Upon quality assessment, the studies demonstrated a range of moderate to low risk of bias (ROB), ensuring a comprehensive evaluation of the evidence base. Meta-analysis showed that individuals with type 2 diabetes mellitus had a higher shown high association (OR = 2.07 (0.52–8.18) and risk (RR =1.31 (0.70–2.43) for the development of OC compared to nondiabetics ( P > 0.05). The funnel plot did show presence of possible publication bias in meta-analysis. Conclusion: It was found that DM patients were at higher risk and more associated with the development of OC. However, as OC is multifactorial disease, the presence of a single factor cannot have a significant effect on disease progression. Therefore, furthermore prospective studies with a greater sample size and follow-up period should be conducted so as to validate the findings of this study.
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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 | 0.019 | 0.042 |
| Meta-epidemiology (narrow) | 0.003 | 0.002 |
| Meta-epidemiology (broad) | 0.018 | 0.039 |
| Bibliometrics | 0.007 | 0.009 |
| Science and technology studies | 0.001 | 0.001 |
| Scholarly communication | 0.004 | 0.002 |
| Open science | 0.002 | 0.001 |
| 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".