Human papillomavirus prevalence and dynamics
Bibliographic record
Abstract
OBJECTIVES: To estimate the prevalence and dynamics of human papillomavirus (HPV) infection, over a 5-year period, among Saudi women. METHODS: A 2-phase, population-based study combining cross-sectional and cohort designs was carried out with 5360 ever-married women aged 30-65 from Jeddah, Saudi Arabia, between 2013 and 2018. Participants were enrolled in a designated screening program and screened using the hybrid capture 2 HPV test. Women testing positive for HPV were followed up after one year to estimate the HPV clearance rate, while those testing negative had a follow-up after 5 years to assess new HPV infections. Factors associated with HPV positivity and clearance, including sociodemographic and clinical aspects, were analyzed. RESULTS: Participant's mean age was 44.3 and the average marriage duration was 22.6 years. The initial HPV prevalence was 4.7%. After one year, the HPV clearance rate among initially positive women was 84.3%. The rate of new HPV infections among initially negative women after 5 years was 0.2%, resulting in a cumulative HPV prevalence of 5% over the study period. The incidence rate was estimated at 47 per 100,000 person-years. Parity was the only independent factor inversely associated with HPV positivity, with an odds ratio of 0.93 (95% confidence interval: 0.8 - 0.99). CONCLUSION: The prevalence of HPV in Saudi women was relatively low, suggesting a low transmission rate of HPV. This finding indicates the need for continuous monitoring and tailored prevention strategies.
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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 | 0.002 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.001 | 0.001 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.001 | 0.001 |
| Open science | 0.000 | 0.000 |
| Research integrity | 0.000 | 0.000 |
| 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".