Prevalence and Clustering Patterns of Human Papillomavirus Genotypes in Multiple Infections
Bibliographic record
Abstract
Prevalence of multiple human papillomavirus (HPV) infections, involvement of specific HPV phylogenetic clades in multiple infections, and clustering patterns of multiple infections at the clade level were assessed in 854 HIV (-) and 275 HIV (+) women cross-sectionally. Reverse line blot assay was used to detect 27 HPV genotypes. Involvement of specific clades in coinfections and clustering patterns were assessed using HPV clade/genotype as the unit of analyses. Expected frequencies assuming independence for all possible clade combinations in two-genotype infections were derived using a multinomial expansion and comparisons of observed and expected frequencies were done using a composite goodness-of-fit test. In all, 100 two-genotype infections were detected; 61 in HIV (-) and 39 in HIV (+) women. Clade A9 (HPV types 16, 31, 33, 35, 52, and 58) was significantly less likely to be involved in multiple infections compared with all other clades (55.2% versus 64.6%; adjusted odds ratios, 0.68; 95% confidence interval, 0.48-0.95). Observed patterns for all possible clade combinations (among HPV clades A3, A5, A6, A7, A9, and A10) in two-genotype infections did not significantly differ from those expected in the entire sample, across HIV, Pap smear, and age strata (all goodness-of-fit exact P > 0.20). These results indicate that clade A9 is less likely to be involved in multiple infections and that HPV genotypes predominantly establish multiple infections at random, with little positive/negative clustering for either phylogenetically related or unrelated types. The current method of analysis affords the opportunity to test clustering of a large number of HPV genotype/clade combinations at nominal alpha levels.
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How this classification was reachedexpand
Full frame distilled prediction
Teacher imitationNot calibrated prevalence, not ground truth. Human validation pending. Learned from the 10,348 direct Codex labels and 10,348 direct Gemma labels. Candidate is the union of thresholded teacher heads; consensus is their intersection. These outputs are machine_predicted_unvalidated and are not human labels or direct frontier model labels.
Codex and Gemma teacher scores by category
| Category | Codex | Gemma |
|---|---|---|
| Metaresearch | 0.001 | 0.000 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.000 | 0.000 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.000 | 0.000 |
| Open science | 0.000 | 0.000 |
| Research integrity | 0.000 | 0.000 |
| 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 teacher head, 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".