The role of human papillomavirus in head and neck cancer in Senegal
Bibliographic record
Abstract
BACKGROUND: Exploring the presence and role of human papillomavirus (HPV) in head and neck cancer (HNC) is a necessary step to evaluate the potential impact of HPV prophylactic vaccines. OBJECTIVE: To assess the prevalence and oncogenic role of HPV in HNC in Senegal. METHODS: This is a multicenter cross-sectional study. Paraffin-embedded blocks of cases diagnosed with invasive HNC between 2002 and 2010 were collected from 4 pathology laboratories in Senegal. Presence of HPV DNA was determined by PCR and DEIA, and genotyping performed with LiPA25. Tubulin analysis was performed to assess DNA quality. HPV DNA-positive cases were tested for p16INK4a expression. FINDINGS: A total of 117 cases were included in the analysis: 71% were men, mean age was 52 years old (SD ±18.3), and 96% of cases were squamous cell carcinoma. Analysis was performed on 41 oral cavity tumors, 64 laryngeal tumors, 5 oropharyngeal tumors and 7 pharyngeal tumors. Only four cases (3.4%; 95% CI = 0.9%-8.5%) harbored HPV DNA. HPV types detected were HPV16, HPV35 and HPV45. However, among HPV-positive cases, none showed p16INK4a overexpression. CONCLUSION: Our findings indicate that HPV DNA prevalence in HNC in Senegal is very low, suggesting that HPV is not a strong risk factor for these cancers. Additional larger studies are needed to confirm these findings and explore other potential risk factors specific to the region.
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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) | 0.000 | 0.000 |
| Bibliometrics | 0.002 | 0.003 |
| Science and technology studies | 0.001 | 0.001 |
| Scholarly communication | 0.001 | 0.001 |
| Open science | 0.000 | 0.001 |
| Research integrity | 0.000 | 0.000 |
| Insufficient payload (model declined to judge) | 0.002 | 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".