Costs and Benefits of Opportunistic Salpingectomy as an Ovarian Cancer Prevention Strategy
Bibliographic record
Abstract
OBJECTIVE: To conduct a cost-effectiveness analysis of opportunistic salpingectomy (elective salpingectomy at hysterectomy or instead of tubal ligation). METHODS: A Markov Monte Carlo simulation model estimated the costs and benefits of opportunistic salpingectomy in a hypothetical cohort of women undergoing hysterectomy for benign gynecologic conditions or surgical sterilization. The primary outcome measure was the incremental cost-effectiveness ratio. Effectiveness was measured in terms of life expectancy gain. Sensitivity analyses accounted for uncertainty around various parameters. Monte Carlo simulation estimated the number of ovarian cancer cases associated with each strategy in the Canadian population. RESULTS: Salpingectomy with hysterectomy was less costly ($11,044.32 ± $1.56) than hysterectomy alone ($11,206.52 ± $29.81) or with bilateral salpingo-oophorectomy ($12,626.84 ± $13.11) but more effective at 21.12 ± 0.02 years compared with 21.10 ± 0.03 and 20.94 ± 0.03 years, representing average gains of 1 week and 2 months, respectively. For surgical sterilization, salpingectomy was more costly ($9,719.52 ± $3.74) than tubal ligation ($9,339.48 ± $26.74) but more effective at 22.45 ± 0.02 years compared with 22.43 ± 0.02 years (average gain of 1 week) with an incremental cost-effectiveness ratio of $27,278 per year of life gained. Our results were stable over a wide range of costs and risk estimates. Monte Carlo simulation predicted that salpingectomy would reduce ovarian cancer risk by 38.1% (95% confidence interval [CI] 36.5-41.3%) and 29.2% (95% CI 28.0-31.4%) compared with hysterectomy alone or tubal ligation, respectively. CONCLUSION: Salpingectomy with hysterectomy for benign conditions will reduce ovarian cancer risk at acceptable cost and is a cost-effective alternative to tubal ligation for sterilization. Opportunistic salpingectomy should be considered for all women undergoing these surgical procedures.
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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 | 0.000 | 0.001 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 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 | 0.000 |
| 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".