Occurrence of Oral Premalignant Lesions Among Tobacco Users in a Tribal Population: A Systematic Review and Meta-Analysis
Bibliographic record
Abstract
This review aimed to comprehensively assess the association between tobacco use and oral health outcomes, specifically the presence of premalignant lesions (PMLs), through a synthesis of multiple assessments conducted in diverse populations. A systematic search of relevant literature was performed, and studies meeting the inclusion criteria were selected using appropriate Medical Subject Headings (MeSH) words and Boolean operators. Data from these studies was pooled and analysed using Review Manager 5.4 (The Cochrane Collaboration, The Nordic Cochrane Centre, Copenhagen). The Newcastle-Ottawa scale was used to assess the methodological quality of the studies included. The pooled analysis of the five selected papers revealed a significant correlation between tobacco use and an increased prevalence of PMLs among tobacco users. Tobacco users had an odds ratio of 15.22 (95% CI: 10.01-23.15) as compared to non-user cohorts, significant at p 0.0001. This comprehensive synthesis of assessments underscores the detrimental impact of tobacco use on oral health, particularly in terms of potentially malignant lesions. The findings emphasise the urgency of targeted public health interventions to address tobacco consumption and promote oral health awareness, especially in populations with high tobacco consumption rates. Standardisation of methodologies and representation of diverse populations in future research would strengthen the evidence base and facilitate more effective strategies to improve oral health outcomes globally.
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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 | 0.011 | 0.032 |
| Meta-epidemiology (narrow) | 0.002 | 0.001 |
| Meta-epidemiology (broad) | 0.015 | 0.020 |
| Bibliometrics | 0.008 | 0.009 |
| Science and technology studies | 0.001 | 0.001 |
| Scholarly communication | 0.003 | 0.002 |
| Open science | 0.002 | 0.002 |
| Research integrity | 0.002 | 0.001 |
| 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".