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Record W7161805039 · doi:10.82308/25810

Cutaneous human papillomaviruses in head and neck cancers: risk factors or innocent bystanders

2022· dissertation· en· W7161805039 on OpenAlexaboutno aff
Walid Al-Soneidar

Bibliographic record

Venuenot available
Typedissertation
Languageen
FieldMedicine
TopicHead and Neck Cancer Studies
Canadian institutionsnot available
Fundersnot available
KeywordsHead and neck cancerHead and neckHuman papillomavirusCancerGenotypeReferralHPV infection

Abstract

fetched live from OpenAlex

In high-income countries, human papillomavirus (HPV) is a major cause of head and neck cancers (HNC). While high-risk types from the α-genus like HPV16 have been studied extensively in the HNC literature, the role of other genera (β and γ), also called cutaneous HPV, is still poorly understood. However, recent studies have shown that β- and γ-HPV could be related to cancers in the skin, esophagus, and head and neck. There are few studies investigating their role in HNC, and none in the Canadian population. This dissertation research aims to address limitations in previous work and advance the research on the relation between cutaneous HPV and HNC. The data for this project come from the Head and Neck Cancer (HeNCe) Life study. HeNCe investigators recruited incident HNC cases (460) and controls (458), frequency-matched by age and sex, from four main referral hospitals in Montreal, Canada. HeNCe collected information on sociodemographic and behavior characteristics using in-person interviews, and tested rinse and brush specimens for HPV genotyping. Tumor samples were retrieved from hospital archives for a subsample of cases (n=121) to investigate HPV in tumor tissues. Samples were tested for all three genera of HPV using several molecular techniques. First, we describe the prevalence of HPV genera and genotypes in oral and tumor samples and examine the distribution according to age, sex, sexual behavior, smoking, alcohol consumption, and oral health indicators. Similar to the α-genus, γ-HPV distribution varied by smoking and sexual behavior. However, β-HPV did not show a difference in distribution by any of the typical cancer risk factors except for age. Second, we estimated confounding-adjusted odds ratios (aOR) and 95% confidence intervals (CI) for the effect of HPV on HNC using logistic regression. α-HPV genus had a strong effect on HNC, particularly HPV16 (aOR=22.6; 95% CI: 10.8, 47.2). We found weaker evidence for γ-HPV (aOR= 1.29; 95% CI: 0.80, 2.08) and β-HPV was more common among controls than cases (aOR=0.80; 95% 0.57, 1.11). We conducted a quantitative bias analysis for the relation between HPV16 and HNC and found the effect would be underestimated when not accounting for the three epidemiologic biases: unmeasured confounding, selection bias, and measurement error. Multiple bias analyses for HPV16 increased the strength of the point estimate but also increased uncertainty (aOR=54.2, 95%CI 10.7, 385.9).Finally, we estimated the interaction between HPV genera in HNC, particularly the interaction between HPV16 and infection with any β- or γ-HPV. Infection with HPV16 alone had a strong effect on HNC. The effect of coinfection between HPV16 and any cutaneous HPV was stronger than the effect of either one alone, but we did not find strong evidence for an additive interaction as the study was underpowered. However, the point estimate for interaction between HPV16 and any cutaneous HPV infection was positive with relative excess risk due to interaction (RERI) = 2.44 (95% CI -23.27, 28.15). Likewise, we did not find strong evidence for the interaction between HPV16 and β-HPV or γ-HPV, but the point estimate was in a negative direction with any β-HPV and a positive direction for any γ-HPV infection. Because of the limited sample size, results were imprecise and definite conclusions cannot be made

Fetched live from OpenAlex and de-inverted. Abstracts are not stored in this database: the inverted indexes are 8.6 GB of the frame’s 9.3 GB of text, and the host has 13 GB free.

How this classification was reachedexpand

Full frame machine prediction

Teacher imitation

Not 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.

metaresearch head score (Codex)0.001
metaresearch head score (Gemma)0.002
Version: metacan-v3-hybrid-931329e0061cValidation status: machine_predicted_unvalidated
Candidate categoriesnone
Consensus categoriesnone
DomainCandidate signal: none · Consensus signal: none
Study designCandidate signal: Observational · Consensus signal: Observational
GenreCandidate signal: Empirical · Consensus signal: Empirical
Teacher disagreement score0.006
Threshold uncertainty score0.018

Distilled classifier scores by category (both heads)

CategoryCodexGemma
Metaresearch0.0010.002
Meta-epidemiology (narrow)0.0000.000
Meta-epidemiology (broad)0.0000.000
Bibliometrics0.0010.001
Science and technology studies0.0010.001
Scholarly communication0.0010.001
Open science0.0000.001
Research integrity0.0010.001
Insufficient payload (model declined to judge)0.0050.000

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.

Opus teacher head0.042
GPT teacher head0.360
Teacher spread0.318 · how far apart the two teachers sit on this one work
Validation statusscore_only:v0-immature-baseline · verbatim from the scoring run: score_only means the number may rank works, and no category label ships from it

Classification

machine, unvalidated

Machine predicted; a candidate call from one source (direct Gemma or distilled Codex), not a consensus.

The models applied no category: nothing in the taxonomy fit this work.
Study designObservational
Domainnot available
GenreEmpirical

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".

Quick stats

Citations0
Published2022
Admission routes1
Has abstractyes

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