Factors associated with human papillomavirus (HPV) virus load variations in genital infections in young women
Bibliographic record
Abstract
Abstract Human papillomaviruses (HPVs) are the most oncogenic viruses known to humans, with 12 high-risk (HR) genotypes causing nearly all cervical cancers. Cytology is commonly used to screen for cervical lesions but is currently being replaced by testing for high-risk HPV (HR HPV). Although HR HPV screening has a higher sensitivity, its specificity is limited, and it is currently advised to repeat the first screening 4 to 6 months later. To increase the sensitivity of the screening triage, other biomarkers have been suggested, including HPV viral load. Indeed, since 1999, several independent studies have found an association between HR HPV viral load in cervical samples and the severity of cervical disease. Here, we further explore the determinants of variations in HPV viral load in genital infections in young adult women. We analysed samples collected in the PAPCLEAR clinical cohort for participants who were infected by HPV genotypes for which we quantified virus load using qPCR targeting 13 genotypes. We developed a Bayesian statistical model estimating the effect of covariates of interest on the HPV viral load. To analyse precisely the viral load difference between HPV genotypes, phylogenetic distances between HPVs were also integrated in the Bayesian model. Our results fail to identify an effect of anti-HPV vaccination, co-infections by multiple HPVs or tobacco smoking on the detected viral load. On the opposite, swabs contained significantly more viral copies than cervical smears. Our results also highlight that most of the viral load variance could be explained at the genotype level (80%) rather than at the individual level (20%). Our model reveals important differences in viral load detected between the different genotypes tested, with HPV16 being the highest and HPV18 the lowest. The impact of phylogenetic signal on viral load was also estimated to be low, except for a cluster comprised of HPV53, HPV66 and HPV56. These results contribute to identifying the main drivers of HPV viral load detected and could help design needed future screening policies.
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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 | 0.003 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.000 | 0.001 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.001 | 0.000 |
| Open science | 0.000 | 0.000 |
| 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".