A health economic model to estimate the costs and benefits of an mRNA vs DNA high-risk HPV assay in a hypothetical HPV primary screening algorithm in Ontario, Canada
Bibliographic record
Abstract
This study models the impact of using two different types of high-risk (HR) human papillomavirus (HPV) tests: mRNA (Aptima) and DNA (Hybrid Capture 2) as part of a hypothetical primary HPV screening program in Ontario, Canada. Outcomes were the costs of the screening program, and number of colposcopies, HPV tests and cytology tests. Results were estimated for one cohort going through the screening algorithm. A decision tree model was adapted from a published UK study, with inputs drawn from published Canadian data for the probabilities through the model, costs, demographic, and screening data from Ontario. Sensitivity and scenario analyses explored uncertainty in the model inputs and assumptions. Results indicated that screening using an mRNA test could yield cost savings of CAD $4,007,266 (95% credibility interval [CI]: -7,866,251 - 8,035) compared to using a DNA test, with 10,639 (95% CI: 10,170 - 11,094) fewer women undergoing unnecessary colposcopies, and reductions in unnecessary HR-HPV and cytology tests. The HR-HPV test comprised the largest percentage of the costs saved, and the probability of being HPV positive in the first year had the biggest impact on results. These results indicate that the choice of HR-HPV test is important when implementing a primary HPV screening program to avoid unnecessary resource use and cost, which will benefit both women and healthcare providers.
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How this classification was reachedexpand
Full frame distilled prediction
Teacher imitationNot calibrated prevalence, not ground truth. Human validation pending. Learned from the 10,348 direct Codex labels and 10,348 direct Gemma labels. Candidate is the union of thresholded teacher heads; consensus is their intersection. These outputs are machine_predicted_unvalidated and are not human labels or direct frontier model labels.
Codex and Gemma teacher scores by category
| Category | Codex | Gemma |
|---|---|---|
| Metaresearch | 0.002 | 0.000 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.001 | 0.000 |
| Bibliometrics | 0.000 | 0.000 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.000 | 0.000 |
| Open science | 0.000 | 0.000 |
| Research integrity | 0.000 | 0.001 |
| Insufficient payload (model declined to judge) | 0.000 | 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 teacher head, 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".