Prevalence and Distribution of Oral Mucosal Lesions in a Geriatric Indian Population
Bibliographic record
Abstract
BACKGROUND: Oral health is important to individuals of all age groups. Previous epidemiologic studies of the oral health status of the general population in India provided very little information about oral mucosal lesions in the elderly. Hence, the purpose of the present study was to determine the prevalence of the oral lesions in a geriatric Indian population. METHODS: 5,100 patients were clinically evaluated, with age ranging from 60 to 98 years. There were 3,100 males and 2,000 females, with a mean age of 69 ± 6.3 yrs. The statistical analysis was done using the SPSS software, where p < .05 was considered to be significant. RESULTS: 64% of the patients presented with one or more oral lesions, associated to tobacco, betel nut consumption, and lesions secondary to trauma and prosthesis. Males were more affected than females and this difference was clinically not significant (p > .05). The lesions were more frequently observed between 65 to 70 yrs. The most common alterations observed were smoker's palate (43%), denture stomatitis (34%), oral submucous fibrosis (30%), frictional keratosis (23%), leukoplakia (22%), and pyogenic granuloma (22%). Hard palate was the most commonly affected site (23.1%). CONCLUSIONS: The findings of the present study provide important information when clinically evaluating oral cavity in elderly. Close follow-up and systematic evaluation is required in the elderly population to plan future treatment needs.
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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 | 0.001 |
| 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 | 0.000 | 0.000 |
| Open science | 0.000 | 0.000 |
| Research integrity | 0.000 | 0.000 |
| Insufficient payload (model declined to judge) | 0.001 | 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".