Determinants of Human Papillomavirus Coinfections among Montreal University Students: The Influence of Behavioral and Biologic Factors
Bibliographic record
Abstract
BACKGROUND: Human papillomavirus (HPV) coinfections are common among HPV-infected individuals, but the significance and etiology of these infections remain unclear. Though current evidence suggests that women with coinfections have increased HPV exposure (i.e., more sexual partners), it is also hypothesized that these women may represent a subgroup with increased biologic susceptibility. This study sought to examine determinants of coinfections in a cohort of young women, examining both behavioral and biologic factors related to HPV acquisition over time. METHODS: Female university students (n = 537) in Montreal, Canada, were followed for 2 years at 6-month intervals. At each visit, cervical specimens were collected for cytology and HPV testing, and women completed a questionnaire about lifestyle and behavior. HLA alleles were typed from purified DNA collected from cervical specimens. Two definitions of coinfections were used: cumulative coinfection over follow-up and concurrent coinfection at each visit. Multiple logistic regression was used to determine predictors of both cumulative and concurrent coinfections using baseline and time-dependent covariates. RESULTS: The most consistent determinant of coinfection occurrence was number of sexual partners, though several genes of the immune response (HLA-DQB1*06:02, HLA-G*01:01:03, and HLA-G*01:01:05) were also identified as significant predictors of cumulative coinfections. CONCLUSIONS: HPV coinfections mainly occur due to increased sexual activity, but biologic susceptibility may also be involved in a subset of women. Immunologic factors may put women at greater risk of coinfections over the long term, but short-term risk is almost exclusively driven by modifiable sexual behaviors. IMPACT: Additional research should continue to further identify immunologic biomarkers of HPV susceptibility.
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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 | 0.002 |
| 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 | 0.001 | 0.000 |
| Research integrity | 0.000 | 0.001 |
| Insufficient payload (model declined to judge) | 0.003 | 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".