Cost effectiveness analysis of cervical cancer screening in women until age 70
Bibliographic record
Abstract
Background: 2017 US Preventive Services Task Force guidelines for screening cervical cancer and pre-malignant lesions advise that screenings cease for women over age 65, with qualifications. Recent literature has identified significant discrepancies in rates of cervical cancer in older women – if hysterectomies in this patient population is accounted for, cervical cancer incidence does not decline with age as previously established. This adjusted incidence of cervical cancer necessitates a re-examination of current practice.Methods: This study seeks to demonstrate the utility of extending the cervical cancer screening age recommendations to age 70. Cost effectiveness will be estimated, from a payer perspective, of extending screening to age 70 for the United States women’s population in those who have not undergone hysterectomy or otherwise been treated for past cervical cancer or premalignancy. A Markov model was constructed to project outcomes in a hypothetical cohort of 10 000 women aged 65 to 70, with a time horizon of lifetime. A Probability Sensitivity Analysis determined the robustness of the result, and the Incremental Cost-effectiveness Ratio (ICER) is charted.Results: The economic evaluation of screening compared to none in this population was determined to be cost effective, with an ICER demonstrating a cost benefit, and Quality Adjusted Life Year (QALY) benefit, to extended screening.Conclusions: The sensitivity analysis confirms the robustness of this result. Implementing extended screening guidelines could potentially be a significant gain for both patients and society.
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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 | 0.001 | 0.005 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.001 | 0.001 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.000 | 0.000 |
| Open science | 0.000 | 0.000 |
| Research integrity | 0.000 | 0.000 |
| Insufficient payload (model declined to judge) | 0.002 | 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".