Using the Cancer Risk Management Model to Evaluate the Health and Economic Impacts of Cytology Compared with Human Papillomavirus DNA Testing for Primary Cervical Cancer Screening in Canada
Bibliographic record
Abstract
BACKGROUND: In Canada, discussion about changing from cytology to human papillomavirus (hpv) dna testing for primary screening in cervical cancer is ongoing. However, the Canadian Task Force on Preventive Health Care has not yet made a recommendation, concluding that the evidence is insufficient. METHODS: We used the cervical cancer and hpv transmission models of the Cancer Risk Management Model to study the health and economic outcomes of primary cytology compared with hpv dna testing in 14 screening scenarios with varying screening modalities and intervals. Projected cervical cancer cases, deaths, colposcopies, screens, costs, and incremental cost-effectiveness were evaluated. We performed sensitivity analyses for hpv dna test costs. RESULTS: Compared with triennial cytology from age 25, 5-yearly hpv dna screening alone from age 30 resulted in equivalent incident cases and deaths, but 55% (82,000) fewer colposcopies and 43% (1,195,000) fewer screens. At hpv dna screening intervals of 3 years, whether alone or in an age-based sequence with cytology, screening costs are greater, but at intervals of more than 5 years, they are lower. Scenarios on the cost-effectiveness frontier were hpv dna testing alone every 10, 7.5, 5, or 3 years, and triennial cytology starting at age 21 or 25 when combined with hpv dna testing every 3 years. CONCLUSIONS: Changing from cytology to hpv dna testing as the primary screening test for cervical cancer would be an acceptable strategy in Canada with respect to incidence, mortality, screening and diagnostic test volumes.
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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 | 0.003 | 0.009 |
| Meta-epidemiology (narrow) | 0.002 | 0.001 |
| Meta-epidemiology (broad) | 0.001 | 0.002 |
| Bibliometrics | 0.002 | 0.002 |
| Science and technology studies | 0.001 | 0.001 |
| Scholarly communication | 0.002 | 0.001 |
| Open science | 0.003 | 0.001 |
| Research integrity | 0.002 | 0.002 |
| Insufficient payload (model declined to judge) | 0.005 | 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".