Geriatric oral lesions: A multicentric study
Bibliographic record
Abstract
AIM: To carry out an oral biopsy survey in geriatric patients from the participating institutions. METHODS: The biopsy records of the participating institutions were reviewed for oral lesions from patients aged 65 years and older diagnosed from 2003 to 2012. Demographic data and the site of the lesions were collected. Histopathological diagnoses were categorized into two categories: non-neoplastic lesions (reactive/inflammatory lesion, cyst, allergic/immunologic disorders, potentially malignant disorders, infection and others) and neoplastic lesions (benign and malignant tumors). Data were analyzed by appropriate statistics using stata11. RESULTS: Of the 76,045 accessioned cases, 11,346 cases (14.92%) were in geriatric patients. The mean age of the patients was 72.98 ± 6.25 years. A total of 5010 cases (44.16%) were diagnosed in males, whereas 6336 cases (55.84%) were diagnosed in females. The male-to-female ratio was 0.79:1. Non-neoplastic lesions outnumbered the neoplastic counterpart. The five most prevalent oral lesions in the geriatric population in the present study in descending order of frequency were squamous cell carcinoma, focal fibrous hyperplasia (irritation fibroma), radicular cyst, osteomyelitis and epithelial dysplasia, respectively. The site of predilection was labial/buccal mucosa, followed by gingiva, mandibular bone, tongue and maxillary bone, respectively. CONCLUSIONS: The geriatric oral lesions from the present study showed a similar trend with studies based on histopathological data, but different from the studies based on clinical data. This study also shed more light on potentially malignant disorders, as well as benign and malignant tumors.
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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 | 0.001 | 0.001 |
| Meta-epidemiology (narrow) | 0.001 | 0.000 |
| Meta-epidemiology (broad) | 0.001 | 0.000 |
| Bibliometrics | 0.002 | 0.002 |
| Science and technology studies | 0.001 | 0.000 |
| Scholarly communication | 0.001 | 0.001 |
| Open science | 0.000 | 0.001 |
| 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".