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 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 | 0.021 | 0.057 |
| Meta-epidemiology (narrow) | 0.001 | 0.000 |
| Meta-epidemiology (broad) | 0.001 | 0.001 |
| Bibliometrics | 0.002 | 0.002 |
| Science and technology studies | 0.001 | 0.001 |
| Scholarly communication | 0.002 | 0.002 |
| Open science | 0.002 | 0.002 |
| Research integrity | 0.001 | 0.001 |
| 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".