Type‐Specific Duration of Human Papillomavirus Infection: Implications for Human Papillomavirus Screening and Vaccination
Bibliographic record
Abstract
BACKGROUND: Understanding the duration of human papillomavirus (HPV) infection may help find suitable end points for vaccine trials and testing intervals in screening studies. We studied genotype-specific infection duration among 2462 women enrolled in the Ludwig-McGill cohort study. METHODS: Cervical specimens collected every 4-6 months were tested by a polymerase chain reaction protocol. Actuarial techniques were used to estimate the duration of HPV infection and to investigate the influence of age, number of sexual partners, and coinfection with multiple HPV types. RESULTS: At enrollment, the prevalence of infection with high-risk HPV types was 10.6%, and the prevalence of infection with low-risk HPV types was 6.1%; incidence rates were 6.1 and 5.0 infections per 1000 women-months, respectively. Prevalent infections took longer to clear than incident infections (mean time to clearance, 18.6 months vs. 13.5 months). The mean duration of incident infection with high- and low-risk HPV varied according to the analytic approach used to measure this variable and showed considerable variation by HPV type (range, 5.1-15.4 months). Age and number of partners did not influence infection duration, whereas coinfection was associated with increased infection duration. The mean duration of HPV-16 monoinfection was 11.0 months, and the mean duration of HPV-16 coinfection was 15.4 months. CONCLUSION: There was considerable variation among HPV types with regard to the duration of infection. Coinfection with multiple types contributed to an increased infection duration.
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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 | 0.007 | 0.031 |
| Meta-epidemiology (narrow) | 0.000 | 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 | 0.000 |
| Research integrity | 0.001 | 0.001 |
| Insufficient payload (model declined to judge) | 0.004 | 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".