Prevalence of human papilloma virus 16 and 18 in oral squamous cell carcinoma patients: A systematic review
Bibliographic record
Abstract
Background: HPV 16 and 18 are the two strains of Human Papillomavirus that have been associated with the development of OSCC. The aim of this study was to assess the prevalence of HPV 16 and18 in Oral Squamous Cell Carcinoma patients in comparison to controls. Data Sources: Literature search was conducted on PubMed, Scopus, and Google Scholar. All relevant studies till May 2025 were included. The search strategy combined Medical Subject Headings and relevant keywords. PRISMA guidelines were followed. Study Selection: Studies conducted on OSCC patients in which prevalence of HPV 16 and 18 was studied in comparison to controls were included. Studies that assessed the prevalence of HPV 16 and 18 in HNSCC, pre-malignant lesions, any other carcinoma and or strain of HPV other than 16 and 18 were excluded. Data Extraction: Data included publication title, year, authors, study design, sample size, prevalence of HPV 16 & 18 in cases and controls, number of cases and controls and detection methods. Quality assessment was done using Newcastle Ottawa scale. Data Synthesis: HPV 16 had higher prevalence in OSCC patients as compared to controls. Both the virus strains were seldom found in healthy controls. Conclusion: HPV 16 can have a significant role to play in OSCC development. HPV 16 (and HPV 18 to a lesser extent) is more prevalent in OSCC. Therefore, it may not be the primary cause but it has role to play in the development of OSCC. Keywords: Oral Squamous Cell Carcinoma, Human Papilloma virus, Polymerase chain reaction
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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 | 0.006 | 0.028 |
| Meta-epidemiology (narrow) | 0.001 | 0.001 |
| Meta-epidemiology (broad) | 0.007 | 0.006 |
| Bibliometrics | 0.012 | 0.014 |
| Science and technology studies | 0.001 | 0.001 |
| Scholarly communication | 0.002 | 0.002 |
| Open science | 0.002 | 0.001 |
| Research integrity | 0.002 | 0.001 |
| Insufficient payload (model declined to judge) | 0.004 | 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".