Why this work is in the frame
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Bibliographic record
Abstract
BACKGROUND: To determine the prevalence and clinicopathologic features of the oral cancer patients. MATERIAL AND METHODS: Biopsy records of the participating institutions were reviewed for oral cancer cases diagnosed from 2005 to 2014. Demographic data and site of the lesions were collected. Sites of the lesion were subdivided into lip, tongue, floor of the mouth, gingiva, alveolar mucosa, palate, buccal/labial mucosa, maxilla and mandible. Oral cancer was subdivided into 7 categories: epithelial tumors, salivary gland tumors, hematologic tumors, bone tumors, mesenchymal tumors, odontogenic tumors, and others. Data were analyzed by descriptive statistics using SPSS software version 17.0. RESULTS: Of the 474,851 accessioned cases, 6,151 cases (1.30%) were diagnosed in the category of oral cancer. The mean age of the patients was 58.37±15.77 years. A total of 4,238 cases (68.90%) were diagnosed in males, whereas 1911 cases (31.07%) were diagnosed in females. The male-to-female ratio was 2.22:1. The sites of predilection for oral cancer were tongue, labial/buccal mucosa, gingiva, palate, and alveolar mucosa, respectively. The three most common oral cancer in the descending order of frequency were squamous cell carcinoma, non-Hodgkin lymphoma and mucoepidermoid carcinoma. CONCLUSIONS: Although the prevalence of oral cancer is not high compared to other entities, oral cancer pose significant mortality and morbidity in the patients, especially when discovered late in the course of the disease. This study highlights some anatomical locations where oral cancers are frequently encountered. As a result, clinicians should pay attention to not only teeth, but oral mucosa especially in the high prevalence area as well since early detection of precancerous lesions or cancers in the early stage increase the chance of patient being cured and greatly reduce the mortality and morbidity. This study also shows some differences between pediatric and elderly oral cancer patients as well as between Asian and non-Asian oral cancer patients.
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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 | 0.002 | 0.002 |
| Meta-epidemiology (narrow) | 0.001 | 0.001 |
| Meta-epidemiology (broad) | 0.004 | 0.001 |
| Bibliometrics | 0.000 | 0.000 |
| Science and technology studies | 0.002 | 0.002 |
| Scholarly communication | 0.000 | 0.001 |
| Open science | 0.002 | 0.001 |
| Research integrity | 0.001 | 0.002 |
| Insufficient payload (model declined to judge) | 0.002 | 0.001 |
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 it