Exploring monitoring strategies for population surveillance of HPV vaccine impact using primary HPV screening
Bibliographic record
Abstract
Australia's cervical screening program transitioned from cytology to HPV-testing with genotyping for HPV16/18 in Dec'2017. We investigated whether program data could be used to monitor HPV vaccination program impact (commenced in 2007) on HPV16/18 prevalence and compared estimates with pre-vaccination benchmark prevalence. Pre-vaccination samples (2005-2008) (n = 1933; WHINURS), from 25 to 64-year-old women had been previously analysed with Linear Array (LA). Post-vaccination samples (2013-2014) (n = 2989; Compass pilot), from 25 to 64-year-old women, were analysed by cobas 4800 (cobas), and by LA for historical comparability. Age standardised pre-vaccination HPV16/18 prevalence was 4.85% (95%CI:3.81-5.89) by LA; post-vaccination estimates were 1.67% (95%CI:1.21-2.13%) by LA, 1.49% (95%CI:1.05-1.93%) by cobas, and 1.63% (95%CI:1.17-2.08%) for cobas and LA testing of non-16/18 cobas positives (cobas/LA). Age-standardised pre-vaccination oncogenic HPV prevalence was 15.70% (95%CI:13.79-17.60%) by LA; post-vaccination estimates were 9.06% (95%CI:8.02-10.09%) by LA, 8.47% (95%CI:7.47-9.47%) by cobas and cobas/LA. Standardised rate ratios between post-vs. pre-vaccination rates were significantly different for HPV16/18, non-16/18 HPV and oncogenic HPV: 0.34 (95%CI:0.23-0.50), 0.68 (95%CI:0.55-0.84) and 0.58 (95%CI:0.48-0.69), respectively. Additional strategies (LA for all cobas positives; combined cobas and LA results on all samples) had similar results. If a single method is applied consistently, it will provide important data on relative changes in HPV prevalence following vaccination.
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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 | 0.003 | 0.001 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.001 | 0.002 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.000 | 0.000 |
| Open science | 0.000 | 0.000 |
| Research integrity | 0.000 | 0.001 |
| 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".