The role of HPV infection in oropharyngeal cancer.
Bibliographic record
Abstract
BACKGROUND: Human papilloma virus (HPV)-positive oropharyngeal squamous cell carcinoma (OPSCC) has been recognized as a distinct disease entity associated with oral HPV infection with high-risk serotypes, mainly among white man at younger age. Lifetime number of oral sex partners of HPV-positive OPSCC patients is the strongest risk factor associated. HPV type 16 is now established as oncogenic and it is the main cause (over the 80%) of this type of OPSCC, followed by HPV 18 (3%). Nowadays, it is reported a dramatic rising of HPV-positive OPSCC, mainly in developed countries, including Australia, Canada, Denmark, Netherlands, Norway, Sweden, Poland, Slovakia, Switzerland, Estonia, France, Japan, United States (US) and United Kingdom. At present, the yearly number of new incidence OPSCC cases given to HPV worldwide has been estimated of 29 000 by the International Agency for Research on Cancer (IARC). If incidence trends continue, the annual number of HPV-positive oropharyngeal cancers is expected to overcome the annual number of cervical cancers by the year 2020, in the US. AIM: The aim of this paper is to review the recent data about several topics including risk factors of HPV-positive OPSCCs, guidelines in diagnostic evaluation, treatment, prognosis and prevention strategies, through prophylactic HPV vaccine on both sexes. Nowadays, HPV detection is a clinical standard of care for oropharyngeal malignancy by reporting tumors as HPV positive or p16 positive.
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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 | 0.001 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.001 | 0.000 |
| Bibliometrics | 0.001 | 0.002 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.001 | 0.001 |
| Open science | 0.000 | 0.000 |
| Research integrity | 0.001 | 0.001 |
| Insufficient payload (model declined to judge) | 0.002 | 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".