Surveillance After Treatment for Cervical Intraepithelial Neoplasia
Bibliographic record
Abstract
OBJECTIVE: To estimate outcomes and costs of surveillance strategies after treatment for high-grade cervical intraepithelial neoplasia (CIN). METHODS: A hypothetical cohort of women was evaluated after treatment for CIN 2 or 3 using a Markov model incorporating data from a large study of women treated for CIN, systematic reviews of test accuracy, and individual preferences. Surveillance strategies included initial conventional or liquid-based cytology, human papillomavirus testing, or colposcopy 6 months after treatment, followed by annual or triennial cytology. Estimated outcomes included CIN, cervical cancer, cervical cancer deaths, life expectancy, costs, cost per life-year, and cost per quality-adjusted life-year. RESULTS: Conventional cytology at 6 and 12 months, followed by triennial cytology, was least costly. Compared with triennial cytology, annual cytology follow-up reduced expected cervical cancer deaths by 73% to 77% and had an average incremental cost per life-year gained of $69,000 to $81,000. For colposcopy followed by annual cytology, the incremental cost per life-year gained ranged from $70,000 to more than $1 million, depending on risk. Between-strategy differences in mean additional life expectancy per woman were less than 4 days; differences in mean incremental costs per woman were as high as $822. In the cost-utility analysis, colposcopy at 6 months followed by annual cytology had an incremental cost per quality-adjusted life-year of less than $5,500. Human papillomavirus testing or liquid-based cytology added little to no improvement to life-expectancy with higher costs. CONCLUSION: Annual conventional cytology surveillance reduced cervical cancers and cancer deaths compared with triennial cytology. For high risk of recurrence, a strategy of colposcopy at 6 months increased life expectancy and quality-adjusted life expectancy. Human papillomavirus testing and liquid-based cytology increased costs, but not effectiveness, compared with traditional approaches.
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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 | 0.004 | 0.017 |
| Meta-epidemiology (narrow) | 0.001 | 0.000 |
| Meta-epidemiology (broad) | 0.001 | 0.004 |
| Bibliometrics | 0.001 | 0.001 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.001 | 0.001 |
| Open science | 0.001 | 0.001 |
| Research integrity | 0.001 | 0.001 |
| Insufficient payload (model declined to judge) | 0.004 | 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".