Assessment of the Influence of Risk Factors on the Incidence of Oral Squamous Cell Carcinoma (OSCC) in the Northern Pakistani Population
Bibliographic record
Abstract
Background: Oral squamous cell carcinoma (OSCC) is the most common type of oral cancer, accounting for over 94 percent of all cases, with substantial disparities in occurrence among South Asian countries. In Pakistan its occurrence rate rises from last two decade. A combination of risk factor habit, duration and intensity doubles the rate of incidence. Methods: Fully informed consent was taken. Both genders having age 18-55 years and cases confirmed by biopsy reports were included in the study. A thorough history of the disease and risk factors were taken and labelled (smoking, alcohol consumption, and betel nut chewing) accordingly. Data was analysed using SPSS 20. Gender base stratification was done using chi-square test and significance was defined as a P value of ? 0.05. Results: In present study 110 (73.3%) male and 40 (26.7%) female patients. Mean ±SD of age of OSCC patients was 47.00± 9.170 years. Among the participants 9 (6.0%) smokers, 39 (26.0%) consumed alcohol, use betel and 24 (16.0%) areca nut pan. Association of smoking with gender was statistically significant (p=0.013). Similarly, betel nut chewing also showed statistically significant correlation with gender (p=0.044). Both alcohol and snuff did not show any statistically significant correlation with gender. Conclusion: The public should be aware of the association of the risk factors and development of oral cancer. Public health measures should be taken to prevent smoking and chewing tobacco.
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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 | 0.003 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.001 | 0.001 |
| Science and technology studies | 0.001 | 0.000 |
| Scholarly communication | 0.001 | 0.000 |
| Open science | 0.000 | 0.000 |
| Research integrity | 0.000 | 0.001 |
| Insufficient payload (model declined to judge) | 0.003 | 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".