Prevalence of Human Papillomavirus‐Associated Head and Neck Cancer in Rwanda: A 10‐Year Review
Bibliographic record
Abstract
INTRODUCTION: Head and neck cancer (HNC) is a significant global health burden, with late presentation leading to complex treatment. While human papillomavirus (HPV) infection has been implicated in HNC, data from low- and middle-income countries (LMICs) are limited. In this study, we investigated the prevalence and role of HPV in head and neck cancers diagnosed in Rwanda. METHODS: A retrospective cross-sectional study was conducted using Rwanda Cancer Registry from January 2011 through December 2020. p16 immunohistochemistry as a surrogate for HPV was performed on a randomly selected case. p16-positive cases were genotyped. RESULTS: A total of 1001 patients with HNC were identified; 82% (n = 819) had squamous cell carcinoma. The mean age at diagnosis was 51.1 years, with a majority being males (58%). Oral cavity and lip (27%) were the most common primary cancer sites. Stage was unknown in most cases (75%, n = 747). HIV status was known in 33% (n = 334) of patients with 10% (n = 33) HIV-positive; 22% of 202 randomly selected cases were p16-positive; 34% of the p16-positive cases were oropharynx. PCR analysis of p16-positive cases showed 19% HPV positivity, and HPV16 was the most common high-risk HPV strain, and 55.5% were recorded HPV-positive by PCR. CONCLUSIONS: HNC cases in Rwanda have been increasing from 2011 to 2020, with a significant portion being HPV-positive. Strategies to implement routine testing for p16, especially in oropharynx cancer patients, improved preservation of tissue samples, collection of comprehensive information including cancer risk factors, staging, and treatment are needed in Rwanda.
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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 | 0.002 | 0.004 |
| Meta-epidemiology (narrow) | 0.001 | 0.001 |
| Meta-epidemiology (broad) | 0.001 | 0.001 |
| Bibliometrics | 0.005 | 0.006 |
| Science and technology studies | 0.000 | 0.001 |
| Scholarly communication | 0.001 | 0.002 |
| Open science | 0.001 | 0.001 |
| Research integrity | 0.001 | 0.001 |
| 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".