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Record W4410607379 · doi:10.3389/froh.2025.1604925

Knowledge of HPV and its association with oropharyngeal cancer among dental students: a systematic review and meta-analysis

2025· review· en· W4410607379 on OpenAlexaboutno aff
Khaled Albusairi, Badriyah Mandani, Ward Bouresly, Yash Brahmbhatt, Hend Alqaderi, Hesham Alhazmi

Bibliographic record

VenueFrontiers in Oral Health · 2025
Typereview
Languageen
FieldMedicine
TopicHead and Neck Cancer Studies
Canadian institutionsnot available
Fundersnot available
KeywordsMeta-analysisAssociation (psychology)MedicineCancerOncologyPsychologyInternal medicine

Abstract

fetched live from OpenAlex

Background Human papillomavirus (HPV) infection is a significant risk factor for oropharyngeal cancer (OPC), yet dental students' knowledge of this association varies widely. Given the critical role dentists play in early detection and prevention, understanding their level of knowledge is essential. This study systematically reviews existing research to assess dental students' awareness of HPV and its link to OPC. Methods A systematic review and meta-analysis were conducted following PRISMA guidelines. PubMed, ProQuest, and Web of Science databases were searched for studies published up to August 2023. The Newcastle-Ottawa Scale was used to evaluate study quality. A random effects model was applied to calculate pooled prevalence with 95% confidence intervals. Results Sixteen studies, comprising 6,345 participants, were included. The pooled analysis showed that 69% of dental students had general knowledge of HPV (range: 56%–96.5%; 95% CI: 0.56–0.81), while 77% recognized its association with OPC (range: 18%–96.4%; 95% CI: 0.63–0.89). Significant heterogeneity was observed across studies ( Q = 646.34, P < 0.001 for HPV; Q = 804.07, P < 0.001 for HPV-OPC). Conclusion Knowledge gaps among dental students may hinder prevention efforts. Standardized education in dental curricula is crucial to ensure future dentists are well-prepared to address HPV-related conditions and promote early detection in clinical practice.

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 distilled prediction

Teacher imitation

Not calibrated prevalence, not ground truth. Human validation pending. Learned from the 10,348 direct Codex labels and 10,348 direct Gemma labels. Candidate is the union of thresholded teacher heads; consensus is their intersection. These outputs are machine_predicted_unvalidated and are not human labels or direct frontier model labels.

metaresearch head score (Codex)0.001
metaresearch head score (Gemma)0.000
Version: codex-gemma-dda1882f352aValidation status: machine_predicted_unvalidated
Candidate categoriesnone
Consensus categoriesnone
DomainCandidate signal: none · Consensus signal: none
Study designCandidate signal: Systematic review · Consensus signal: none
GenreCandidate signal: Review · Consensus signal: Review
Teacher disagreement score0.739
Threshold uncertainty score0.974

Codex and Gemma teacher scores by category

CategoryCodexGemma
Metaresearch0.0010.000
Meta-epidemiology (narrow)0.0000.000
Meta-epidemiology (broad)0.0140.001
Bibliometrics0.0000.001
Science and technology studies0.0000.000
Scholarly communication0.0000.000
Open science0.0000.000
Research integrity0.0000.000
Insufficient payload (model declined to judge)0.0000.000

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 head0.068
GPT teacher head0.433
Teacher spread0.365 · 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 teacher head, not a consensus.

The models applied no category: nothing in the taxonomy fit this work.
Study designSystematic review
Domainnot available
GenreReview

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
Published2025
Admission routes1
Has abstractyes

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