Strategies to accelerate the elimination of cervical cancer in British Columbia, Canada: a modelling study
Bibliographic record
Abstract
BACKGROUND: To eliminate cervical cancer in Canada by 2040, defined as an annual age-standardized incidence rate (ASIR) lower than 4.0 per 100 000 women, the Canadian Partnership Against Cancer (CPAC) identified 3 priorities for action: increasing human papillomavirus (HPV) vaccine coverage, implementing HPV-based screening and increasing screening participation, and improving follow-up after abnormal screen results. Our objective was to explore the impact of these priorities on the projected time to elimination of cervical cancer in British Columbia. METHODS: We used OncoSim-Cervical, a microsimulation model led and supported by CPAC and developed by Statistics Canada that simulates HPV transmission and the natural history of cervical cancer for the Canadian population. We updated model parameters to reflect BC's historical participation rates and program design. We simulated the transition to HPV-based screening and developed scenarios to explore the additional impact of achieving 90% vaccination coverage, 95% screening recruitment, 90% ontime screening, and 95% follow-up compliance. We projected cervical cancer incidence, ASIR, and year of elimination for the population of BC for 2023-2050. RESULTS: HPV-based screening at current vaccination, participation, and follow-up rates can eliminate cervical cancer by 2034. Increasing on-time screening and follow-up compliance could achieve this target by 2031. Increasing vaccination coverage has a small impact over this time horizon. INTERPRETATION: With the implementation of HPV-based screening, cervical cancer can be eliminated in BC before 2040. Efforts to increase screening participation and follow-up through this transition could potentially accelerate this timeline, but the transition from cytology- to HPV-based screening is fundamental to achieving this goal.
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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 | 0.001 | 0.003 |
| Meta-epidemiology (narrow) | 0.001 | 0.001 |
| Meta-epidemiology (broad) | 0.001 | 0.001 |
| Bibliometrics | 0.001 | 0.002 |
| Science and technology studies | 0.002 | 0.001 |
| Scholarly communication | 0.002 | 0.001 |
| Open science | 0.003 | 0.001 |
| Research integrity | 0.001 | 0.001 |
| Insufficient payload (model declined to judge) | 0.006 | 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".