Oral Squamous Cell Carcinomas are Associated with Poorer Outcome with Increasing Ages.
Bibliographic record
Abstract
OBJECTIVES: 1.1.Although oral cancers traditionally occur in people between the age of 50 and 70, there are increasing incidences of this disease in younger and very old people. Objectives: to compare the demographics, habits, clinicopathological features, treatment and outcome of oral cancer in three age groups of patients: Young (≤ 45), Traditional (46 to 75), and Old (> 75). SUBJECTS: 1.2.Primary oral cancers (393 patients) in a longitudinal study were used. RESULTS: 1.3.Significant differences were noted in ethnicity (fewer Caucasian patients in Young), tobacco habit (more non-smokers in Young), location of cancer (more at tongue for Young and more at low-risk sites for Old) and treatment (more surgery for Young). Compared to Young (univariate analysis), Traditional and Old showed a 3- and 4.5-fold increase in local recurrences respectively; 1.9- and 2.7-fold increase in regional metastasis; 3.1- and 5.4-fold increase in death due to disease; and a 3.4- and 6.6-fold decrease in overall survival. Compared to Young (multivariate analysis), Traditional and Old showed a 2.4- and 3.3-fold increase in local recurrence; 2.7- and 5.4-fold increase in disease-specific survival; and 2.8- and 6.5-fold decrease in overall survival. CONCLUSION: 1.4.Oral cancer in different age groups showed differing ethnicity, habit, location, treatment and outcome.
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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 | 0.000 | 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) | 0.004 | 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 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".