Human papillomavirus and Epstein‐Barr virus co‐infection in oral and oropharyngeal squamous cell carcinomas: A systematic review and meta‐analysis
Bibliographic record
Abstract
Squamous cell carcinoma of the oral cavity (OSCC) is the most common head-and-neck malignancy. Importantly, we are experiencing an alarming rise in the incidence of oropharyngeal squamous cell carcinoma (OPSCC) globally. Oncogenic viruses, human papillomavirus (HPV) and Epstein-Barr virus (EBV), are known to be co-associated with OSCC and OPSCC cases. However, the reported incidence of HPV and EBV co-infection in OSCCs and OPSCCs globally is unknown. To address this, we performed a formal meta-analysis and systematic review on published studies that report the detection of both EBV and HPV in OSCCs and OPSCCs. Our analysis revealed 18 relevant studies out of a total of 1820 cases (1181 from the oral cavity and 639 from the oropharynx). Overall, HPV and EBV co-infection was found in 11.9% of OSCC and OPSCC cases combined (95% CI: 8%-14.1%). Based on anatomical subsite, dual positivity estimates were 10.5% (95% CI: 6.7%-15.1%) for OSCC and 14.2% (95% CI: 9.1%-21.3%) for OPSCC. The highest dual positivity rates described were in European countries: for OSCC 34.7% (95% CI: 25.9%-44.6%) in Sweden and for OPSCC, 23.4% (95% CI: 16.9%-31.5%) in Poland. Given these substantive prevalence rates, the value of detecting dual infection in the diagnosis and prognosis of these cancers deserves careful longitudinal studies, as do implications for cancer prevention and therapy. We further proposed molecular mechanisms that could explain how HPV and EBV could co-contribute to the aetiology of OSCCs and OPSCCs.
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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 | 0.008 | 0.022 |
| Meta-epidemiology (narrow) | 0.002 | 0.001 |
| Meta-epidemiology (broad) | 0.013 | 0.025 |
| Bibliometrics | 0.006 | 0.008 |
| Science and technology studies | 0.001 | 0.001 |
| Scholarly communication | 0.003 | 0.001 |
| 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".