A Systematic Review and Meta-Analysis Assessing the Role of Oral Health as a Risk Factor in Oral Cancer
Bibliographic record
Abstract
Oral squamous cell carcinoma is the leading type of cancer in Southeast Asian countries and many parts of the world. Many factors increase the risk of oral cancer, like tobacco, betel nuts, alcohol consumption, sharp teeth, infections, and other factors. Oral health-related issues have been reported in many studies of oral cancer, but there is a need to understand the role of the same as a risk factor. The systematic review and meta-analysis were conducted to assess the role of oral health as a risk factor in oral cancer. The population diagnosed with oral cancer (P) of all age groups and both gender, exposure (E) is oral health (includes poor oral hygiene, periodontal disease, and other oral diseases excluding oral potentially malignant disorders (OPMD)), the comparator (C), is patients without oral health issues, outcome (O) is the role of poor oral health as a risk factor for oral cancer. A systematic review and meta-analysis were conducted. The databases used for the search were PubMed, Cochrane Database, Embase, Scopus, and Google Scholar. The unpublished reports, reviews, and grey literature were considered. Case-control studies were included assessing poor oral health as a risk factor with odds ratio as an effective measure. Newcastle Ottawa Scale for risk of bias in the case-control study was considered. The study results showed that tooth loss odds ratio (OR)=1.13, CI (0.99-1.26), I2 value of 71.7%, Oral hygiene OR=1.29, CI (1.04-1.54), I2 value of 19.7% and in periodontal diseases OR=2.14 CI (1.70-2.58), I2 value of 75.3% had a higher risk of developing oral cancer. The risk factors for tooth loss and periodontal disease showed moderate heterogenicity and less heterogenicity for oral hygiene. Poor oral health factors such as periodontal disease, poor oral hygiene, and loss of teeth show higher odds of oral cancer than the control. The periodontal disease shows the highest odds than other factors. These risk factors can be considered for the primordial prevention of oral cancer.
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How this classification was reachedexpand
Full frame distilled prediction
Teacher imitationNot calibrated prevalence, not ground truth. Human validation pending. Learned from the 10,348 direct Codex labels and 10,348 direct Gemma labels. Candidate is the union of thresholded teacher heads; consensus is their intersection. These outputs are machine_predicted_unvalidated and are not human labels or direct frontier model labels.
Codex and Gemma teacher scores by category
| Category | Codex | Gemma |
|---|---|---|
| Metaresearch | 0.001 | 0.001 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.013 | 0.002 |
| Bibliometrics | 0.000 | 0.001 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.000 | 0.000 |
| Open science | 0.000 | 0.000 |
| Research integrity | 0.000 | 0.000 |
| Insufficient payload (model declined to judge) | 0.000 | 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 teacher head, 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".