Risk-Reducing Salpingectomy: Considerations from an OBGYN Perspective
Bibliographic record
Abstract
Abstract Background With recent evidence that opportunistic salpingectomy is effective in preventing high grade serous carcinoma, it is imperative to consider the optimal use of this procedure. In this research, we investigated the opinions of obstetrician-gynecologists (OBGYNS) about the acceptability of using salpingectomy as a stand-alone surgery for people at higher-than-average lifetime risk (but without a pathogenic variant that increases risk for ovarian cancer) known as ‘risk-reducing salpingectomy’(RRS). Methods We conducted semi-structured interviews with purposefully sampled practicing OBGYNs in the province of British Columbia, Canada. We used qualitative interpretive description with inductive thematic analysis for data analysis. Our work was informed by the theoretical framework of acceptability. Results The nineteen participants included physicians from both general obstetrics and gynecology practices, as well as subspecialties. OBGYNs generally found RRS to be acceptable, though this acceptability was conditional on clinical, patient, and system-level factors. Five major themes suggest that: 1) There are risks and benefits of RRS, that if balanced might support acceptability; 2) It is important to define and identify the correct patient for RRS; 3) OBGYNs value patient autonomy in the decision to undergo RRS; 4) Reproductive justice and equity are intertwined and influenced by the history of forced and coerced sterilization; and 5) Formal guidance and the right environment are enablers of RRS. Conclusions This work provides initial evidence that, from the OBGYN perspective, RRS is acceptable in the right patient population, with considerations from those practicing on the front lines of 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 | 0.017 | 0.022 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.001 | 0.001 |
| Science and technology studies | 0.007 | 0.017 |
| Scholarly communication | 0.006 | 0.003 |
| Open science | 0.002 | 0.004 |
| Research integrity | 0.003 | 0.005 |
| Insufficient payload (model declined to judge) | 0.003 | 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".