Prevalence of Oral Potentially Malignant Disorders in Smokeless Tobacco Users With or Without Areca Nut: A Meta‐Analysis
Bibliographic record
Abstract
BACKGROUND: Smokeless tobacco (SLT) use is a major global risk factor for oral potentially malignant disorders (OPMDs). However, the differential impact of SLT product composition, particularly tobacco-only versus combined tobacco-areca nut products, on OPMD prevalence remains inadequately characterized. OBJECTIVE: To compare the pooled prevalence of OPMDs between users of tobacco-only SLT and users of SLT containing both tobacco and areca nut. METHODS: This study was conducted following PRISMA guidelines. Electronic databases (PUBMED, Cochrane, Scopus, Embase, Web of Science) were searched until December 15, 2024. Included studies reported OPMD prevalence among current SLT users with a clear product description. Areca nut-only products were excluded. Two reviewers independently screened studies, extracted data, and assessed quality (Newcastle-Ottawa scale). Pooled prevalence estimates were calculated using random-effects models in R software due to anticipated heterogeneity. Sensitivity analysis (leave-one-out) was performed. RESULTS: Thirty-three studies (62 680 SLT users: 5058 tobacco-only; 57 622 tobacco-areca nut) were included. Overall OPMD prevalence was significantly higher among SLT with areca nut users (16.3%, 95% CI) compared to tobacco-only SLT users (10.4%, 95% CI). Tobacco-areca nut use showed markedly high prevalence of oral submucous fibrosis (OSMF) (33%) and dysplasia (16%), especially in endemic regions like India (88.8% OPMD prevalence in this subgroup). Tobacco-only SLT use was predominantly associated with leukoplakia (18%) and lichen planus/lichenoid reactions (15%). A critical limitation was the high proportion of non-specific OPMD diagnoses (27% overall, 38% in tobacco-areca nut users), hindering precise estimates of specific conditions. Sensitivity analyses confirmed result robustness. CONCLUSIONS: This meta-analysis demonstrates a substantial global burden of OPMDs among SLT users, with distinct risk profiles driven by product composition. Tobacco-areca nut SLT poses the highest risk (especially for OSMF and dysplasia), while tobacco-only SLT remains a significant independent risk factor (primarily for leukoplakia and lichenoid reaction). The high prevalence, particularly with combined products in regions like South Asia, underscores the urgent need for targeted public health interventions. Future research must prioritize precise product classification and standardized OPMD diagnosis to improve risk assessment.
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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 | 0.014 | 0.026 |
| Meta-epidemiology (narrow) | 0.003 | 0.002 |
| Meta-epidemiology (broad) | 0.015 | 0.064 |
| Bibliometrics | 0.006 | 0.006 |
| Science and technology studies | 0.001 | 0.001 |
| Scholarly communication | 0.003 | 0.002 |
| Open science | 0.002 | 0.002 |
| Research integrity | 0.002 | 0.002 |
| Insufficient payload (model declined to judge) | 0.004 | 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".