Occurrence of Cervical Infection with Multiple Human Papillomavirus Types is Associated with Age and Cytologic Abnormalities
Bibliographic record
Abstract
BACKGROUND: Few aspects of the occurrence of infections with multiple HPV types have been described. Since the immunity conferred by vaccines is type-specific, the epidemiology of such coinfections needs to be addressed. GOAL: The goal of the study was to document the prevalence and incidence of infection with multiple HPV types and the distribution of HPV types in coinfections. STUDY DESIGN: In a prospective cohort of 2075 Brazilian women, cervical specimens were collected for cytology and HPV detection. Information on potential risk factors was obtained by interview. RESULTS: The prevalence of HPV coinfections was 3% among cytologically normal women, 10% among women with ASCUS, 23% among those with LSIL, and 7% among those with HSIL. The incidence rate of coinfection declined markedly with age (Ptrend<0.001). Some HPV types co-occurred less frequently than expected, namely, HPV 16 and 18 occurring with other oncogenic HPV types and HPV 6/11. CONCLUSION: We have observed that occurrence of HPV coinfection was dependent both on age and on the presence of cytologic abnormalities. These results may have implications for vaccine development and for public health decisions about vaccination programs.
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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 | 0.006 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.001 | 0.001 |
| 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) | 0.004 | 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".