Evaluating Ovarian Cancer Risk–Reducing Salpingectomy Acceptance: A Survey
Bibliographic record
Abstract
ABSTRACT: With evidence that salpingectomy is effective in preventing high-grade serous carcinoma, it is time to consider offering this procedure to people at higher-than-average lifetime risk for ovarian cancer, despite not having a pathogenic genetic variant that increases the risk for ovarian cancer. This targeted approach has potential to be effective at reducing ovarian cancer incidence, and unlike opportunistic salpingectomy, it is focused on people with an increased lifetime risk of ovarian cancer. However, the acceptability of this approach within the population of potential patients remains unknown. We conducted an online survey of adults in British Columbia, Canada, who were defined as “at risk” for ovarian cancer (i.e., people born with ovaries). Participants completed a questionnaire on demographics, ovarian cancer risk and protective factors, concerns about risk-reducing salpingectomy (RSS), and the risk they considered high enough to warrant RRS. We included 211 participants. Among these participants, 42% (n = 88) indicated that they would consider RRS at any lifetime risk or any risk above the population average. Another 20 participants chose risks between 1.5% and 4% for a cumulative 51% of the sample choosing risks below thresholds for oophorectomy. In contrast, 6% (n = 12) indicated that they would not consider the procedure at any risk level. None of the factors collected were associated with the likelihood that a person would find RRS acceptable. Overall, our participants showed broad interest in RRS as an ovarian cancer prevention strategy. These results suggest that there would likely be uptake if RRS was offered. SIGNIFICANCE: This study found that many participants were willing to consider RRS to prevent ovarian cancer. Further research on RRS should be undertaken to understand how this can be best used for ovarian cancer prevention.
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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 | 0.007 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.001 |
| Bibliometrics | 0.001 | 0.001 |
| Science and technology studies | 0.001 | 0.000 |
| Scholarly communication | 0.001 | 0.001 |
| Open science | 0.000 | 0.001 |
| Research integrity | 0.001 | 0.001 |
| Insufficient payload (model declined to judge) | 0.002 | 0.001 |
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".