Validating the predicted impact of HPV vaccination on HPV prevalence, cervical lesions, and cervical cancer: A systematic review of population level data and modelling studies
Bibliographic record
Abstract
BACKGROUND: We compared model predictions with independently published primary data from population-based studies on the impact of HPV vaccination on HPV prevalence, cervical cancer and its precursors. METHODS: We searched Cochrane Library, EMBASE, MEDLINE, Web of Science for studies concerning high-income countries published between 2005 to June 2, 2023. Relative risk (RR) for HPV-related outcomes comparing the pre-vaccination and post-vaccination periods were collected from observational and modelling studies. The relationship between vaccination coverage and observed relative reductions was determined using meta-regressions, and we compared model prediction to observations. FINDINGS: We identified a total of 5649 potential articles, of which one systematic review, 14 observational studies and 32 modelling studies met our inclusion criteria. A clear relation was found between the RR of HPV diseases related outcomes in the pre- versus post-vaccination era and the vaccination coverage, with 23 out of 28 data points and 19 out of 20 data points showing significant reductions in HPV prevalence and CIN2+ prevalence respectively. Around 67 % (n/N = 12/18) of model predictions were more optimistic on HPV prevalence reductions compared to the 95 % CI of the meta-regression derived from observational studies. For CIN2+ lesions, 48 % (n/N = 31/64) of model predictions for CIN2+ outcomes fell within the 95 % CI. INTERPRETATION: Model predictions and observational data agree that HPV vaccination can have a substantial impact on HPV related outcomes on a population level. Despite large heterogeneity in observational data and modelling studies, it is particularly encouraging that model predictions on the impact of HPV vaccination on CIN2+ model lesions align with observational studies. FUNDING: Ontario Health (formerly known as Cancer Care Ontario).
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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 | 0.050 | 0.140 |
| Meta-epidemiology (narrow) | 0.003 | 0.002 |
| Meta-epidemiology (broad) | 0.012 | 0.025 |
| Bibliometrics | 0.009 | 0.007 |
| Science and technology studies | 0.000 | 0.001 |
| Scholarly communication | 0.004 | 0.002 |
| Open science | 0.004 | 0.002 |
| Research integrity | 0.003 | 0.002 |
| Insufficient payload (model declined to judge) | 0.003 | 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".