Predicting cohort-specific cervical cancer incidence from population-based HPV prevalence surveys: a worldwide study
Bibliographic record
Abstract
Abstract Background Predictions of cervical cancer burden and impact of control measures are often modelled from HPV prevalence. However, predictions could be improved by data on time between prevalent HPV detection and cervical cancer occurrence. Methods Based upon high-risk (HR) HPV prevalence and cervical cancer incidence in the same birth cohorts from 17 worldwide locations, and informed by individual-level data on age at HR HPV detection and on sexual debut, we built a mixed model to predict cervical cancer incidence up to 14 years following prevalent HR HPV detection. Findings Cervical cancer incidence increased significantly during the 14 years following HR HPV detection in women <35 years, e.g. from 0·02 (95% CI 0·003–0·06) per 1000 within 1 year to 2·8 (1·2–6·5) at 14 years for unscreened women, but remained relatively constant following prevalent HR HPV detection above 35 years, e.g. from 5·4 (2·5–11) per 1000 within 1 year to 6·4 (2·4–17·1) at 14 years for unscreened HR HPV positive women aged 45–54 years. Age at sexual debut was a significant modifier of cervical cancer incidence in HR HPV positive women aged <25, but less so at older ages, whereas screening was a modifier in women ≥35 years. Lastly, we predicted annual number and incidence of cervical cancer in ten additional IARC HPV prevalence survey locations without representative cancer incidence data. Interpretation These findings can inform cervical cancer control programmes, particularly in settings without cancer registries, as they allow prediction of future cervical cancer burden from population-based surveys of HPV prevalence. Funding Bill & Melinda Gates Foundation; Canadian Institutes of Health Research.
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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 | 0.004 | 0.011 |
| Meta-epidemiology (narrow) | 0.001 | 0.001 |
| Meta-epidemiology (broad) | 0.001 | 0.002 |
| Bibliometrics | 0.002 | 0.002 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.001 | 0.001 |
| Open science | 0.001 | 0.002 |
| Research integrity | 0.001 | 0.001 |
| Insufficient payload (model declined to judge) | 0.002 | 0.001 |
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".