Beyond Sterilization: A Comprehensive Review on the Safety and Efficacy of Opportunistic Salpingectomy as a Preventative Strategy for Ovarian Cancer
Bibliographic record
Abstract
Ovarian cancer (OC) is Canada’s third most common gynecological cancer, with an estimated 3000 new cases and 1950 deaths projected in 2022. No effective screening has been found to identify OC, especially the most common subtype, high-grade serous carcinoma (HGSC), at an earlier, curable stage. In patients with hereditary predispositions such as BRCA mutations, the rates of HGSC are significantly elevated, leading to the use of risk-reducing salpingo-oophorectomy as the key preventative intervention. Although surgery has been shown to prevent HGSC in high-risk women, the associated premature menopause has adverse long-term sequelae and mortality due to non-cancer causes. The fact that 75% of HGSCs are sporadic means that most women diagnosed with HGSC will not have had the option to avail of either screening or prevention. Recent research suggests that the fimbrial distal fallopian tube is the most likely origin of HGSC. This has led to the development of a prevention plan for the general population: opportunistic salpingectomy, the removal of both fallopian tubes. This article aims to compile and review the studies evaluating the effect of opportunistic salpingectomy on surgical-related complications, ovarian reserve, cost, and OC incidence when performed along with hysterectomy or instead of tubal ligation in the general population.
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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 | 0.001 | 0.003 |
| Meta-epidemiology (narrow) | 0.001 | 0.000 |
| Meta-epidemiology (broad) | 0.002 | 0.002 |
| Bibliometrics | 0.003 | 0.003 |
| 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 | 0.001 |
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".