Evaluation of Human Papillomavirus Type Replacement Postvaccination Must Account for Diagnostic Artifacts: Masking of HPV52 by HPV16 in Anogenital Specimens
Bibliographic record
Abstract
It has been hypothesized that, following a reduction in human papillomavirus (HPV) vaccine-targeted genotypes, an increase in prevalence of other HPV types may occur due to reduced competition during natural infection. Any apparent postvaccination increase must be distinguished from diagnostic artifacts consequent to consensus PCR assays failing to detect HPV types present in low copy numbers in coinfected specimens (under the assumption that with a drop in vaccine-preventable types there may be increased detection of previously "masked" types). We reanalyzed anogenital specimens to evaluate unmasking of HPV52 that may be caused by elimination of HPV16. Using highly sensitive type-specific real-time HPV52 PCR, we retested 1,200 anogenital specimens (all HPV52 negative according to consensus PCR assays) from six epidemiologic studies (200 specimens/study; 100 HPV16(+)/study). Multivariate logistic regression, with adjustment for age and number of sexual partners, was used to evaluate the association between HPV16 positivity and detection of HPV52. In our pooled analysis (n = 1,196), the presence of HPV16 was positively associated with HPV52 detection [adjusted OR, 1.47; 95% confidence interval (CI), 0.76-2.82]. In our separate (study specific) analyses, a statistically significant association was observed in one study that included HIV-infected males (HIPVIRG study; adjusted OR, 3.82; 95% CI, 1.19-12.26). We observed a positive association between HPV16 viral load (tertiles) and detection of HPV52 (P for trend = 0.003). These results indicate that diagnostic artifacts, resulting from unmasking of HPV52, may occur in some settings in the evaluation of HPV type replacement. Additional studies exploring the extent and severity of unmasking are needed.
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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 | 0.022 | 0.037 |
| Meta-epidemiology (narrow) | 0.001 | 0.000 |
| Meta-epidemiology (broad) | 0.001 | 0.001 |
| Bibliometrics | 0.001 | 0.001 |
| Science and technology studies | 0.000 | 0.001 |
| Scholarly communication | 0.001 | 0.001 |
| Open science | 0.001 | 0.001 |
| Research integrity | 0.001 | 0.001 |
| Insufficient payload (model declined to judge) | 0.001 | 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".