Modeling the Balance of Benefits and Harms of Cervical Cancer Screening with Cytology and Human Papillomavirus Testing
Bibliographic record
Abstract
BACKGROUND: Benefits of screening should outweigh its potential harms. We compared various metrics to assess the balance of benefits and harms of cervical cancer screening. METHODS: We used a cervical cancer natural history Markov model calibrated to the Canadian context to simulate 100,000 unvaccinated women over a lifetime of screening with either cytology every 3 years or human papillomavirus (HPV) testing every 5 years. We estimated the balance of benefits and harms attributable to screening using various metrics, including colposcopies/life-year gained, and net lifetime quality-adjusted life-years (QALY) gained, a measure integrating women's health preferences. We present the average (minimum-maximum) model predictions. RESULTS: Cytology-based screening led to 1,319,854 screening tests, 30,395 colposcopies, 13,504 life-years gained over a lifetime, 98 screening tests/life-year gained, 2.3 (1.6-3.3) colposcopies/life-year gained, and a net lifetime gain of 10,735 QALY (5,040-17,797). HPV-based screening with cytology triage in the same population would lead to 698,250 screening tests, 73,296 colposcopies, 15,066 life-years gained over a lifetime, 46 screening tests/life-year gained, 4.9 colposcopies/life-year gained (2.9-11.1), and a net lifetime gain of 11,690 QALY (4,409-18,742). HPV-based screening was predicted to prevent more cancers, but also incur more screening harms than cytology-based screening. CONCLUSIONS: Metrics using colposcopies as the main harm outcome favored cytology-based screening, whereas metrics based on screening tests and health preferences tended to favor HPV-based screening strategies. IMPACT: Whether HPV-based screening will improve the balance between benefits and harms of cervical cancer screening depends on how the balance between benefits and harms is assessed.
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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 | 0.002 | 0.007 |
| Meta-epidemiology (narrow) | 0.001 | 0.001 |
| Meta-epidemiology (broad) | 0.001 | 0.001 |
| Bibliometrics | 0.001 | 0.001 |
| Science and technology studies | 0.001 | 0.001 |
| Scholarly communication | 0.001 | 0.001 |
| Open science | 0.002 | 0.001 |
| Research integrity | 0.002 | 0.001 |
| 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".