Human Papilloma Virus-Associated Oral Pharyngeal Squamous Cell Carcinoma: Prevalence, Prevention, and Awareness of Vaccination in the Indian Population
Bibliographic record
Abstract
Human papilloma virus (HPV), one of the most common sexually transmitted infections, plays a pivotal role in head and neck cancer, primarily oral and oropharyngeal squamous cell carcinomas. HPV is a vaccine-preventable disease that also contributes to cervical cancer. Although HPV vaccination effectively protects the individual against all HPV-associated human carcinomas, the awareness of HPV vaccination and its acceptance is poor in developing nations like India. India has a very high burden of oral cancer, and, unfortunately, the morbidity and mortality rates are also high as the cancer is often detected at an advanced stage. In this review, we explore the prevalence of HPV-associated head and neck squamous cell carcinoma among the Indian population and the awareness of HPV vaccination among Indian youth. Since the prognosis for HPV-associated head and neck squamous cell carcinoma is good, early diagnosis of the cancer is crucial in improving the outcome of the treatment modalities. Efforts are needed to create and increase awareness of HPV vaccination. Routine screening for HPV infection in oral mucosa can prevent the silent epidemic from taking the lives of many young people.
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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 | 0.002 |
| Meta-epidemiology (narrow) | 0.001 | 0.000 |
| Meta-epidemiology (broad) | 0.002 | 0.001 |
| Bibliometrics | 0.002 | 0.003 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.001 | 0.001 |
| Open science | 0.001 | 0.000 |
| Research integrity | 0.001 | 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".