Population-based prevalence of cervical infection with human papillomavirus genotypes 16 and 18 and other high risk types in Tlaxcala, Mexico
Bibliographic record
Abstract
BACKGROUND: Cervical cancer remains an important cause of cancer mortality for Mexican women. HPV 16/18 typing may help to improve cervical cancer screening. Here we present the prevalence of high-risk human papillomavirus (hrHPV) including HPV16 and HPV18 from the FRIDA (Forwarding Research for Improved Detection and Access) population. METHODS: Beginning in 2013, we recruited 30,829 women aged 30-64 in Tlaxcala, Mexico. Cervical samples were collected and tested for 14 hrHPV genotypes (16, 18, 31, 33, 35, 39, 45, 51, 52, 56, 58, 59, 66, and 68). We used logistic regression to estimate odds ratios with 95 % confidence intervals for hrHPV infections according to putative risk factors. RESULTS: Prevalence of infection with any of the 14 hrHPV types was 11.0 %. The age-specific prevalence of all hrHPV formed a U-shaped curve with a higher prevalence for women aged 30-39 and 50-64 than women aged 40-49. Across all age groups, 2.0 % of women were positive for HPV16 and/or HPV18 (HPV16/18), respectively. HPV16/18 prevalence also showed a U-shaped curve with increased prevalence estimates for women aged both 30-39 and 60-64. Both prevalence curves had a significant quadratic age coefficient. Infections with hrHPV were positively associated with an increased number of lifetime sexual partners, a history of sexually transmitted disease, being unmarried, use of hormonal contraception, having a history of smoking and reported condom use in the multivariate model. CONCLUSIONS: The FRIDA population has a bimodal distribution of both hrHPV and HPV16/18 positivity with higher prevalences at ages 30-39 and 60-64. These findings will help to evaluate triage algorithms based on HPV genotyping. TRIAL REGISTRATION: The trial is registered with ClinicalTrials.gov, number NCT02510027 .
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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 | 0.000 | 0.001 |
| 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) | 0.002 | 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".