Screening for HIV-Associated Anal Cancer: Correlation of HPV Genotypes, p16, and E6 Transcripts with Anal Pathology
Bibliographic record
Abstract
BACKGROUND: HIV-positive men with a history of anal-receptive intercourse are at risk for anal cancer. We determined whether human papilloma virus (HPV) biomarkers were correlated with anal pathology in these men. METHODS: HPV genotype was determined by PCR/line blot assay. Real-time PCR assays were done for viral load, E6 transcripts for HPV genotypes 16, 18, and 31, and p16 transcripts. RESULTS: The most common oncogenic HPV types were HPV 16 (38%), 18 (19%), 45 (22%), and 52 (19%). HPV types 16, 18, 31, 52, 59, and 68 were associated with high-grade histology. The number of HPV genotypes per anal swab was higher for anal intraepithelial neoplasia (AIN) 2/3 than for normal or AIN 1 histology [median, 5 types (interquartile range) (IQR), 3-7 versus 3.5 (IQR), 2-6; P = 0.0005]. HPV 16 viral load was also associated with AIN 2/3 histology. There was no difference in p16 or E6 transcripts between histologic grades. In the multivariable logistic regression model, HPV genotypes 16 [odds ratio, 2.58; 95% confidence interval (95% CI), 1.31-5.08; P = 0.006] and 31 (odds ratio, 4.74; 95% CI, 2.00-11.22; P = 0.0004), baseline CD4 count < 400 cells/mm(3) (odds ratio, 2.96; 95% CI, 1.46-5.99; P = 0.0025), and Acquired Immunodeficiency Syndrome (AIDS)-defining illness (odds ratio, 2.42; 95% CI, 1.22-4.82; P = 0.01) were associated with high-grade histology after adjusting for age. CONCLUSIONS: The presence of high-grade anal pathology (AIN 2/3) in HIV-positive men was associated with multiple HPV genotypes, HPV genotypes 16 and 31, and HPV 16 viral load.
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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 | 0.001 | 0.004 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.001 | 0.001 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.001 | 0.001 |
| Open science | 0.000 | 0.000 |
| Research integrity | 0.000 | 0.001 |
| 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".