Prevalence of oral HPV among people living with HIV (PLHIV) in Pune, India
Bibliographic record
Abstract
Background: People living with HIV (PLHIV) are at an increased risk of human papillomavirus (HPV)-related head and neck cancers (HNCs). However, there is little data on the prevalence of oral HPV among PLHIV in India, limiting the planning of oral HPV preventive strategies. Methods: We used cross-sectional data from an oral cancer screening study conducted at the antiretroviral therapy (ART) centre of Byramjee-Jeejeebhoy Government Medical College-Sassoon General Hospitals (BJGMC-SGH). PLHIV ≥21 years of age with no prior history of HNCs were enrolled. We determined the prevalence of high-risk oncogenic HPV (hrHPV) and low-risk non-oncogenic HPV (lrHPV) using real-time PCR and Next-Generation Sequencing. We used multinomial logistic regression to determine the prevalence ratios (PRs) of different sociodemographic, clinical, and behavioural predictors with hrHPV and lrHPV. Multivariable models were adjusted for age, sex, CD4 count and duration on ART. Results: Of the 582 PLHIV enrolled, the median age was 40 years (IQR: 34–46) and 54% were male. More than a fourth (25.8%) had multiple sexual partners and 11% had given oral sex. Median CD4 counts were 510 cells/mm 3 (IQR: 338–700). The prevalence of hrHPV was 4.5% and lrHPV was 3.4%. Of those with hrHPV, 77% had HPV16. There were no significant associations with any predictors for both lrHPV and hrHPV in adjusted analyses. Conclusions: We found the prevalence of any oral HPV (hrHPV and lrHPV) to be 7.9% among PLHIV in India. Larger studies are required to better understand risk factors for oral HPV among Indian PLHIV.
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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 | 0.000 | 0.001 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.001 | 0.001 |
| Science and technology studies | 0.001 | 0.000 |
| Scholarly communication | 0.001 | 0.000 |
| Open science | 0.000 | 0.001 |
| Research integrity | 0.000 | 0.000 |
| 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".