Detection and Clearance of Type-Specific and Phylogenetically Related Genital Human Papillomavirus Infections in Young Women in New Heterosexual Relationships
Bibliographic record
Abstract
BACKGROUND: Understanding the natural history of human papillomavirus (HPV) infections is essential to cervical cancer prevention planning. We estimated HPV type-specific infection detection and clearance in young women. METHODS: The HPV Infection and Transmission among Couples through Heterosexual activity (HITCH) study is a prospective cohort of 502 college-age women who recently initiated a heterosexual relationship. We tested vaginal samples collected at 6 clinical visits over 24 months for 36 HPV types. Using rates and Kaplan-Meier analysis, we estimated time-to-event statistics with 95% confidence intervals (CIs) for detection of incident infections and clearance of incident and present-at-baseline infections (separately). We conducted analyses at the woman- and HPV-levels, with HPV types grouped by phylogenetic relatedness. RESULTS: By 24 months, we detected incident infections in 40.4% (CI, 33.4%-48.4%) of women. Incident subgenus 1 (43.4; CI, 33.6-56.4), 2 (47.1; CI, 39.9-55.5), and 3 (46.6; CI, 37.7-57.7) infections cleared at similar rates per 1000 infection-months. We observed similar homogeny in HPV-level clearance rates among present-at-baseline infections. CONCLUSIONS: Our analyses provide type-specific infection natural history estimates for cervical cancer prevention planning. HPV-level analyses did not clearly indicate that high oncogenic risk subgenus 2 infections persist longer than their low oncogenic risk subgenera 1 and 3 counterparts.
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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 | 0.000 |
| 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) | 0.001 | 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".