Patterns of persistent genital human papillomavirus infection among women worldwide: A literature review and meta-analysis
Bibliographic record
Abstract
Persistent high-risk human papillomavirus (HR-HPV) infection is the strongest risk factor for high-grade cervical precancer. We performed a systematic review and meta-analysis of HPV persistence patterns worldwide. Medline and ISI Web of Science were searched through January 1, 2010 for articles estimating HPV persistence or duration of detection. Descriptive and meta-regression techniques were used to summarize variability and the influence of study definitions and characteristics on duration and persistence of cervical HPV infections in women. Among 86 studies providing data on over 100,000 women, 73% defined persistence as HPV positivity at a minimum of two time points. Persistence varied notably across studies and was largely mediated by study region and HPV type, with HPV-16, 31, 33 and 52 being most persistent. Weighted median duration of any-HPV detection was 9.8 months. HR-HPV (9.3 months) persisted longer than low-risk HPV (8.4 months), and HPV-16 (12.4 months) persisted longer than HPV-18 (9.8 months). Among populations of HPV-positive women with normal cytology, the median duration of any-HPV detection was 11.5 and HR-HPV detection was 10.9 months. In conclusion, we estimated that approximately half of HPV infections persist past 6 to 12 months. Repeat HPV testing at 12-month intervals could identify women at increased risk of high-grade cervical precancer due to persistent HPV infections.
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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 | 0.014 | 0.028 |
| Meta-epidemiology (narrow) | 0.003 | 0.002 |
| Meta-epidemiology (broad) | 0.015 | 0.035 |
| Bibliometrics | 0.010 | 0.013 |
| Science and technology studies | 0.001 | 0.000 |
| Scholarly communication | 0.003 | 0.002 |
| Open science | 0.002 | 0.001 |
| Research integrity | 0.002 | 0.001 |
| 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".