White oral mucosal lesions among the Yemeni population and their relation to local oral habits
Bibliographic record
Abstract
AIMS: The aim of the present study was to assess the prevalence and risk factors of white oral mucosal lesions among Yemeni adults; in particular, those who chew khat and tobacco. METHODS: The present cross-sectional study included 1052 dental patients aged 15 years and older. A detailed oral examination was performed by a single examiner in accordance with standard international criteria. RESULTS: Overall, 25.2% of the study participants presented with one or more white lesions. The most prevalent lesions were khat-induced white lesion (8.8%), leukoedema (5.1%), and frictional keratosis (3.9%). Potentially malignant lesions, such as lichen planus, leukoplakia, and smokeless tobacco-induced lesions, were seen in 2.4%, 1.2%, and 1.7% of participants, respectively. Moreover, three cases of oral cancer were identified. The presence of white lesions was found to be significantly associated with advanced age (P = .004), male gender (P = .009), and khat/tobacco chewing habits (P < .001). CONCLUSIONS: The present study demonstrates a high prevalence of oral benign and potentially malignant white lesions. Further, it highlights the urgent need to develop and implement new government policies to regulate the sale of these products to reduce the prevalence of these lesions and the overall incidence of oral cancers in the Yemeni population.
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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 | 0.000 | 0.000 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.001 | 0.001 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.001 | 0.000 |
| Open science | 0.000 | 0.000 |
| Research integrity | 0.000 | 0.000 |
| Insufficient payload (model declined to judge) | 0.002 | 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".