Prevalence of human papillomavirus in head and neck cancer patients in India: a systematic review and meta-analysis
Bibliographic record
Abstract
BACKGROUND: Human papillomavirus (HPV) is increasingly recognized as a significant risk factor in the development of head and neck cancers (HNCs), with varying prevalence and impact. This study aims to systematically review and analyze the prevalence of HPV in HNCs in India, providing insights into regional variations. METHODS: A comprehensive literature search was carried out using PubMed, Embase, and Web of Science up to November 10, 2023. Inclusion criteria focused on original research reporting HPV-positive cases among HNC patients in India. We used Nested-Knowledge software, for screening, and data extraction. The modified Newcastle-Ottawa Scale was used for quality assessment of included studies. We pooled the prevalence of HPV among HNC patients and performed a random-effects model meta-analysis using R software (version 4.3). RESULTS: The search yielded 33 studies, encompassing 4654 HNC patients. The pooled prevalence of HPV infection was found to be 33% (95% CI: 25.8-42.6), with notable heterogeneity (I² = 95%). Analysis of subgroups according to geographical location indicated varying prevalence rates. Specifically, the prevalence was 47% (95% CI: 32.2-62.4) in the eastern regions and 19.8% (95% CI: 10.8-33.4) in the western regions. No evidence of publication bias was detected. CONCLUSION: The observed considerable regional disparities on the prevalence of HPV in HNC patients in India emphasizes the need for integrated HPV vaccination and screening programs in public health strategies. The findings underline the necessity for further research to explore regional variations and treatment responses in HPV-associated HNCs, considering the impact of factors such as tobacco use and the potential benefits of HPV vaccination.
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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 | 0.012 | 0.029 |
| Meta-epidemiology (narrow) | 0.003 | 0.001 |
| Meta-epidemiology (broad) | 0.017 | 0.040 |
| Bibliometrics | 0.009 | 0.009 |
| Science and technology studies | 0.001 | 0.001 |
| Scholarly communication | 0.003 | 0.002 |
| Open science | 0.002 | 0.002 |
| Research integrity | 0.002 | 0.002 |
| 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".