Estimating the Age of Disease-causal HPV Infection Based on the Natural History of CIN2+ Among Females in Canada
Bibliographic record
Abstract
Background: Although human papillomavirus (HPV) vaccination is approved for males and females up to 45 years of age in Canada, not all of the jurisdictions offer catch-up programs up to age 26. However, US-based modeling studies suggest a significant proportion of causal HPV infections leading to high-grade cervical intraepithelial neoplasia (CIN+) and cervical cancer occur in women older than age 26 years. To inform vaccination policies in Canada, this study estimated the age distribution of putatively causal HPV infections leading to CIN2+ based on the natural history. Methods: We modified an existing discrete event simulation model to estimate the age of causal HPV infection for females diagnosed with CIN2+. Simulated females (n = 1000) were tracked through 3 stages while undergoing screening: causal HPV infection, CIN2+ disease onset, and diagnosis. We identified the age distribution for causal infections that best fit the observed age distribution for CIN2+ diagnosis. Ten independent model runs were conducted to assess reproducibility. Results: The predicted median age at causal HPV infection and CIN2+ diagnosis in Canada was 24.9 (95% confidence interval, 24.3-26.1) and 29.8 years (95% confidence interval, 28.8-30.6), respectively. The model estimated that 84.1% and 47.1% of causal HPV infections occurred in women older than age 18 and 26 years, respectively. Results were stable across 10 model runs. Conclusions: The analysis indicates a substantial percentage of causal HPV infections for CIN2+ occur among women aged 26 years or older. Extending catch-up vaccination programs to women above age 26 years should be considered to prevent these infections and reduce HPV-related cervical diseases.
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How this classification was reachedexpand
Full frame distilled prediction
Teacher imitationNot calibrated prevalence, not ground truth. Human validation pending. Learned from the 10,348 direct Codex labels and 10,348 direct Gemma labels. Candidate is the union of thresholded teacher heads; consensus is their intersection. These outputs are machine_predicted_unvalidated and are not human labels or direct frontier model labels.
Codex and Gemma teacher scores by category
| Category | Codex | Gemma |
|---|---|---|
| Metaresearch | 0.000 | 0.001 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.000 | 0.000 |
| 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) | 0.001 | 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 teacher head, 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".