A comprehensive natural history model of HPV infection and cervical cancer to estimate the clinical impact of a prophylactic HPV‐16/18 vaccine
Bibliographic record
Abstract
The object of our study is to project the impact of a prophylactic vaccine against persistent human papillomavirus (HPV)-16/18 infection on age-specific incidence of invasive cervical cancer. We developed a computer-based mathematical model of the natural history of cervical carcinogenesis to incorporate the underlying type-specific HPV distribution within precancerous lesions and invasive cancer. After defining plausible ranges for each parameter based on a comprehensive literature review, the model was calibrated to the best available population-based data. We projected the age-specific reduction in cervical cancer that would occur with a vaccine that reduced the probability of acquiring persistent infection with HPV 16/18, and explored the impact of alternative assumptions about vaccine efficacy and coverage, waning immunity and competing risks associated with non-16/18 HPV types in vaccinated women. The model predicted a peak age-specific cancer incidence of 90 per 100,000 in the 6th decade, a lifetime cancer risk of 3.7% and a reproducible representation of type-specific HPV within low and high-grade cervical precancerous lesions and cervical cancer. A vaccine that prevented 98% of persistent HPV 16/18 was associated with an approximate equivalent reduction in 16/18-associated cancer and a 51% reduction in total cervical cancer; the effect on total cancer was attenuated due to the competing risks associated with other oncogenic non-16/18 types. A vaccine that prevented 75% of persistent HPV 16/18 was associated with a 70% to 83% reduction in HPV-16/18 cancer cases. Similar effects were observed with high-grade squamous intraepithelial lesions (HSIL) although the impact of vaccination on the overall prevalence of HPV and low-grade squamous intraepithelial lesions (LSIL) was minimal. In conclusion, a prophylactic vaccine that prevents persistent HPV-16/18 infection can be expected to significantly reduce HPV-16/18-associated LSIL, HSIL and cervical cancer. The impact on overall prevalence of HPV or LSIL, however, may be minimal. Based on the relative importance of different parameters in the model, several priorities for future research were identified. These include a better understanding of the heterogeneity of vaccine response, the effect of type-specific vaccination on other HPV types and the degree to which vaccination effect persists over time.
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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 | 0.002 | 0.006 |
| Meta-epidemiology (narrow) | 0.001 | 0.001 |
| Meta-epidemiology (broad) | 0.001 | 0.001 |
| Bibliometrics | 0.001 | 0.001 |
| Science and technology studies | 0.000 | 0.001 |
| Scholarly communication | 0.001 | 0.001 |
| Open science | 0.001 | 0.001 |
| Research integrity | 0.001 | 0.001 |
| 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".