Impact of human papillomavirus (HPV) infection on the development of oral squamous cell carcinoma (OSCC): A systematic review
Bibliographic record
Abstract
This systematic review aimed to identify, select and synthesize clinical studies reporting the prevalence of HPV infection among patients with OSCC, and to determine the odds ratio (OR) of HPV infection in a group of OSCC patients relative to non-OSCC controls through meta-analysis.The study incorporated primary clinical trials that assessed the impact of HPV infection on the development of OSCC. The search was conducted on August 31, 2023, using Bielefeld Academic Search Engine (BASE), as well as PubMed® and Scopus databases. The Newcastle-Ottawa Quality Assessment Scale was used to assess the risk of bias of the included studies. The collected data was then synthesized in the form of tables and a funnel plot. A total of 54 eligible studies were selected for the review, and 10 reports were included in the meta-analysis. Of the 10 papers, 7 reported extractable numerical data on HPV-16 and/or HPV-18 (1,035 patients).The limitations of the evidence included the following: inhomogeneity in terms of HPV type; small number of available controlled studies (not homogeneous in terms of virus type); small number of patients on whom controlled studies were conducted; and the risk of bias related to the selection of study and control groups (present in most studies qualified for the synthesis).In conclusion, HPV is detected by genetic testing in 0.0-74.5% of patients who develop OSCC. The weighted mean OR of detecting HPV-16 or HPV-18 in OSCC patients (OR = 17.1; standard deviation (SD) = 31.4) suggests a potential correlation between these infections and the incidence of OSCC.
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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 | 0.013 | 0.050 |
| Meta-epidemiology (narrow) | 0.002 | 0.002 |
| Meta-epidemiology (broad) | 0.012 | 0.013 |
| Bibliometrics | 0.011 | 0.012 |
| Science and technology studies | 0.001 | 0.001 |
| Scholarly communication | 0.004 | 0.003 |
| Open science | 0.002 | 0.002 |
| Research integrity | 0.003 | 0.001 |
| Insufficient payload (model declined to judge) | 0.003 | 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".